Matching meshes for virtual avatars

ABSTRACT

Examples of systems and methods for matching a base mesh to a target mesh for a virtual avatar or object are disclosed. The systems and methods may be configured to automatically match a base mesh of an animation rig to a target mesh, which may represent a particular pose of the virtual avatar or object. Base meshes may be obtained by manipulating an avatar or object into a particular pose, while target meshes may be obtain by scanning, photographing, or otherwise obtaining information about a person or object in the particular pose. The systems and methods may automatically match a base mesh to a target mesh using rigid transformations in regions of higher error and non-rigid deformations in regions of lower error.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. PatentApplication No. 62/635,939, filed on Feb. 27, 2018, which is entitled“Matching Meshes for Virtual Avatars,” which is hereby incorporated byreference herein in its entirety.

FIELD

The present disclosure relates to virtual reality and augmented reality,including mixed reality, imaging and visualization systems and moreparticularly to rigging systems and methods for animating virtualcharacters, such as avatars.

BACKGROUND

Modern computing and display technologies have facilitated thedevelopment of systems for so called “virtual reality,” “augmentedreality,” and “mixed reality” experiences, wherein digitally reproducedimages are presented to a user in a manner such that they seem to be, ormay be perceived as, real. A virtual reality (VR) scenario typicallyinvolves presentation of computer-generated virtual image informationwithout transparency to other actual real-world visual input. Anaugmented reality (AR) scenario typically involves presentation ofvirtual image information as an augmentation to visualization of theactual world around the user. Mixed reality (MR) is a type of augmentedreality in which physical and virtual objects may co-exist and interactin real time. Systems and methods disclosed herein address variouschallenges related to VR, AR, and MR technology.

SUMMARY

Examples of systems and methods for matching a base mesh to a targetmesh for a virtual avatar are disclosed. The systems and methods may beconfigured to automatically match a base mesh of an animation rig to atarget mesh, which may represent a particular pose of the virtualavatar. Base meshes may be obtained by manipulating an avatar into aparticular pose, while target meshes may be obtain by scanning,photographing, or otherwise obtaining information about a person orobject in the particular pose. The systems and methods may automaticallymatch a base mesh to a target mesh using rigid transformations inregions of higher error and non-rigid deformations in regions of lowererror.

For example, an automated system can match a first mesh to a second meshfor a virtual avatar. The first mesh may represent a base mesh of ananimation rig and the second mesh may represent a target mesh, which mayin some cases be obtained from photogrammetric scans of a personperforming a target pose.

In various implementations, the system can first register the first meshto the second mesh and then conform the first mesh to the second mesh.The system may identify a first set of regions where the first mesh andthe second mesh are not matched to a first error level and a second setof regions where the first mesh and the second mesh are not matched to asecond error level, with the second error level is less than the firsterror level. The system may apply a rigid transformation in the firstset of regions and a non-rigid transformation in the second set ofregions. The system can iterate this transformation process until theerror between the first and the second meshes is less than an errortolerance.

In other implementations, the system may match a first mesh to a secondmesh by matching relatively large subregions and iteratively matchingprogressively smaller subregions until a convergence criterion is met.

In other implementations, the system identifies a first set ofsubregions of a first mesh and a second set of subregions of the firstmesh. For example, the first set and the second set of subregions canform a checkerboard pattern. The system can apply a rigid transformationon the first set of subregions to match the first set of subregions to atarget mesh. The system may match the second set of subregions to thetarget mesh via interpolation. The system may iterate this procedure,e.g., by swapping the first and second sets of subregions.

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims. Neitherthis summary nor the following detailed description purports to defineor limit the scope of the inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Details of one or more implementations of the subject matter describedin this specification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages will becomeapparent from the description, the drawings, and the claims.

FIG. 1 depicts an illustration of a mixed reality scenario with certainvirtual reality objects, and certain physical objects viewed by aperson.

FIG. 2 schematically illustrates an example of a wearable system.

FIG. 3 schematically illustrates example components of a wearablesystem.

FIG. 4 schematically illustrates an example of a waveguide stack of awearable device for outputting image information to a user.

FIG. 5 is a process flow diagram of an example of a method forinteracting with a virtual user interface.

FIG. 6A is a block diagram of another example of a wearable system whichcan comprise an avatar processing and rendering system.

FIG. 6B illustrates example components of an avatar processing andrendering system.

FIG. 7 is a block diagram of an example of a wearable system includingvarious inputs into the wearable system.

FIG. 8 is a process flow diagram of an example of a method of renderingvirtual content in relation to recognized objects.

FIG. 9A schematically illustrates an overall system view depictingmultiple wearable systems interacting with each other.

FIG. 9B illustrates an example telepresence session.

FIG. 10 illustrates an example of an avatar as perceived by a user of awearable system.

FIG. 11A illustrates example meshes of an arm including a base mesh, atarget mesh, and a transfer mesh obtained by matching the base mesh tothe target mesh.

FIG. 11B illustrates example meshes of a shirt including a base mesh, atarget mesh, and a transfer mesh obtained by matching the base mesh tothe target mesh.

FIG. 12A illustrates an example of an application of a rigidtransformation, such as a rigid nearest neighbor transformation, of abase mesh towards a target mesh.

FIG. 12B illustrates an example of an application of a non-rigiddeformation, such as a closest point on surface deformation, of a basemesh towards a target mesh.

FIG. 13 illustrates various examples of different falloff radii inclosest point on surface deformations, or other non-rigid deformations.

FIGS. 14A-14C illustrate example processes for matching base meshes totarget meshes.

Throughout the drawings, reference numbers may be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate example embodiments described herein and are not intended tolimit the scope of the disclosure.

DETAILED DESCRIPTION Overview

A virtual avatar may be a virtual representation of a real or fictionalperson (or creature or personified object) in an AR/VR/MR environment.For example, during a telepresence session in which two AR/VR/MR usersare interacting with each other, a viewer can perceive an avatar ofanother user in the viewer's environment and thereby create a tangiblesense of the other user's presence in the viewer's environment. Theavatar can also provide a way for users to interact with each other anddo things together in a shared virtual environment. For example, astudent attending an online class can perceive and interact with avatarsof other students or the teacher in a virtual classroom. As anotherexample, a user playing a game in an AR/VR/MR environment may view andinteract with avatars of other players in the game.

Embodiments of the disclosed systems and methods may provide forimproved animation of avatars and a more realistic interaction between auser of the wearable system and avatars in the user's environment.Although the examples in this disclosure generally describe animating ahuman-shaped avatar, similar techniques can also be applied to animals,fictitious creatures, objects, etc.

A wearable device can include a display for presenting an interactiveVR/AR/MR environment that includes a high fidelity digital avatar.Creation of a high fidelity digital avatar can take many weeks or monthsof work by a specialized team and can utilize a large number of highquality digitized photographic scans of the human model. Embodiments ofthe disclosed technology have the capability of creating high quality orhigh fidelity avatars (or digital representations in general) of anyhuman, animal, character, or object. In order to accomplish this,embodiments of the disclosed process are faster and less resourceintense (e.g., it may not be practical to put users through the samescanning process a professional model may experience) while stillmaintaining an accurate output.

As an example, a digital representation of a human (generally, anyanimal or deformable object such as clothing or hair) may include askeleton and an overlying mesh (e.g., to show the outer surface, whichmay be skin, clothing, etc.). Each bone can have certain mesh verticesassigned to it, such that when the bone moves, the assigned verticesautomatically move with the bone. This initial movement is called a“skin cluster” and generally captures gross movement. (It should benoted that the bones and skeleton are digital constructs and do notnecessarily correspond to actual bones in the human body.) A subsequentstep in modeling the human, which is sometimes referred to herein as anavatar, may be needed to capture finer movements of the skin, which issometimes referred to herein as a surface or mesh. This subsequent stepis sometimes referred to as a blendshape and represents differences fromthe initial gross movement to capture finer movements of the skin.Blendshapes may need to be obtained for some or all of the differentposes that the digital representation moves into. As an example, a firstblendshape may be needed for animating the digital representation tobend its arm halfway and a second blendshape may be needed for animatingthe digital represent to bend its arm fully.

With the present disclosure, methods and systems are provided forefficiently generating blendshapes for various poses of an avatar ordigital representation of a human or animal (or other object). As anexample, a computing system may obtain a base mesh of an avatar in agiven pose, such as by moving one or more bones in a digitalrepresentation of a human into the given bones (e.g., to capture grossmovement of the digital representation), and may obtain a target mesh inthe given pose, such as by photographing a user (or object) in the givenpose. The computing system may then attempt to match the base mesh tothe target mesh in order to obtain a blendshape for that given pose(e.g., to determine how to adjust the base mesh, via a blendshape, suchthat the animated digital representation in the given pose matches theuser or object in the given pose). The computing system may match thebase mesh to the target mesh by generating a heatmap (which may showregions of higher and lower errors between the base and target meshes),applying rigid transformations moving the base mesh towards the targetmesh in regions of higher error, and applying non-rigid deformationsconforming the base mesh to the target mesh in regions of lower error.These processes may be repeated in an iterative fashion until asatisfactory match is obtained or some other condition is satisfied. Asanother example, the computing system may match subregions of the basemesh to the target mesh, and iteratively match additional subregionsuntil a convergence criterion is met. After a satisfactory match isobtained or some other condition is satisfied, the computing system maygenerate one or more blendshapes for the given pose, which can then beused in refining the digital representation to more accurately reflect areal-world user (or object) in the given pose.

Accordingly, a variety of implementations of systems and methods formatching a first mesh onto a second mesh will be provided below.

Examples of 3D Display of a Wearable System

A wearable system (also referred to herein as an augmented reality (AR)system) can be configured to present 2D or 3D virtual images to a user.The images may be still images, frames of a video, or a video, incombination or the like. At least a portion of the wearable system canbe implemented on a wearable device that can present a VR, AR, or MRenvironment, alone or in combination, for user interaction. The wearabledevice can be used interchangeably as an AR device (ARD). Further, forthe purpose of the present disclosure, the term “AR” is usedinterchangeably with the term “MR”.

FIG. 1 depicts an illustration of a mixed reality scenario with certainvirtual reality objects, and certain physical objects viewed by aperson. In FIG. 1, an MR scene 100 is depicted wherein a user of an MRtechnology sees a real-world park-like setting 110 featuring people,trees, buildings in the background, and a concrete platform 120. Inaddition to these items, the user of the MR technology also perceivesthat he “sees” a robot statue 130 standing upon the real-world platform120, and a cartoon-like avatar character 140 flying by which seems to bea personification of a bumble bee, even though these elements do notexist in the real world.

In order for the 3D display to produce a true sensation of depth, andmore specifically, a simulated sensation of surface depth, it may bedesirable for each point in the display's visual field to generate anaccommodative response corresponding to its virtual depth. If theaccommodative response to a display point does not correspond to thevirtual depth of that point, as determined by the binocular depth cuesof convergence and stereopsis, the human eye may experience anaccommodation conflict, resulting in unstable imaging, harmful eyestrain, headaches, and, in the absence of accommodation information,almost a complete lack of surface depth.

VR, AR, and MR experiences can be provided by display systems havingdisplays in which images corresponding to a plurality of depth planesare provided to a viewer. The images may be different for each depthplane (e.g., provide slightly different presentations of a scene orobject) and may be separately focused by the viewer's eyes, therebyhelping to provide the user with depth cues based on the accommodationof the eye required to bring into focus different image features for thescene located on different depth plane or based on observing differentimage features on different depth planes being out of focus. Asdiscussed elsewhere herein, such depth cues provide credible perceptionsof depth.

FIG. 2 illustrates an example of wearable system 200 which can beconfigured to provide an AR/VR/MR scene. The wearable system 200 canalso be referred to as the AR system 200. The wearable system 200includes a display 220, and various mechanical and electronic modulesand systems to support the functioning of display 220. The display 220may be coupled to a frame 230, which is wearable by a user, wearer, orviewer 210. The display 220 can be positioned in front of the eyes ofthe user 210. The display 220 can present AR/VR/MR content to a user.The display 220 can comprise a head mounted display (HMD) that is wornon the head of the user.

In some embodiments, a speaker 240 is coupled to the frame 230 andpositioned adjacent the ear canal of the user (in some embodiments,another speaker, not shown, is positioned adjacent the other ear canalof the user to provide for stereo/shapeable sound control). The display220 can include an audio sensor (e.g., a microphone) 232 for detectingan audio stream from the environment and capture ambient sound. In someembodiments, one or more other audio sensors, not shown, are positionedto provide stereo sound reception. Stereo sound reception can be used todetermine the location of a sound source. The wearable system 200 canperform voice or speech recognition on the audio stream.

The wearable system 200 can include an outward-facing imaging system 464(shown in FIG. 4) which observes the world in the environment around theuser. The wearable system 200 can also include an inward-facing imagingsystem 462 (shown in FIG. 4) which can track the eye movements of theuser. The inward-facing imaging system may track either one eye'smovements or both eyes' movements. The inward-facing imaging system 462may be attached to the frame 230 and may be in electrical communicationwith the processing modules 260 or 270, which may process imageinformation acquired by the inward-facing imaging system to determine,e.g., the pupil diameters or orientations of the eyes, eye movements oreye pose of the user 210. The inward-facing imaging system 462 mayinclude one or more cameras. For example, at least one camera may beused to image each eye. The images acquired by the cameras may be usedto determine pupil size or eye pose for each eye separately, therebyallowing presentation of image information to each eye to be dynamicallytailored to that eye.

As an example, the wearable system 200 can use the outward-facingimaging system 464 or the inward-facing imaging system 462 to acquireimages of a pose of the user. The images may be still images, frames ofa video, or a video.

The display 220 can be operatively coupled 250, such as by a wired leador wireless connectivity, to a local data processing module 260 whichmay be mounted in a variety of configurations, such as fixedly attachedto the frame 230, fixedly attached to a helmet or hat worn by the user,embedded in headphones, or otherwise removably attached to the user 210(e.g., in a backpack-style configuration, in a belt-coupling styleconfiguration).

The local processing and data module 260 may comprise a hardwareprocessor, as well as digital memory, such as non-volatile memory (e.g.,flash memory), both of which may be utilized to assist in theprocessing, caching, and storage of data. The data may include data a)captured from sensors (which may be, e.g., operatively coupled to theframe 230 or otherwise attached to the user 210), such as image capturedevices (e.g., cameras in the inward-facing imaging system or theoutward-facing imaging system), audio sensors (e.g., microphones),inertial measurement units (IMUs), accelerometers, compasses, globalpositioning system (GPS) units, radio devices, or gyroscopes; or b)acquired or processed using remote processing module 270 or remote datarepository 280, possibly for passage to the display 220 after suchprocessing or retrieval. The local processing and data module 260 may beoperatively coupled by communication links 262 or 264, such as via wiredor wireless communication links, to the remote processing module 270 orremote data repository 280 such that these remote modules are availableas resources to the local processing and data module 260. In addition,remote processing module 280 and remote data repository 280 may beoperatively coupled to each other.

In some embodiments, the remote processing module 270 may comprise oneor more processors configured to analyze and process data or imageinformation. In some embodiments, the remote data repository 280 maycomprise a digital data storage facility, which may be available throughthe internet or other networking configuration in a “cloud” resourceconfiguration. In some embodiments, all data is stored and allcomputations are performed in the local processing and data module,allowing fully autonomous use from a remote module.

Example Components of A Wearable System

FIG. 3 schematically illustrates example components of a wearablesystem. FIG. 3 shows a wearable system 200 which can include a display220 and a frame 230. A blown-up view 202 schematically illustratesvarious components of the wearable system 200. In certain implements,one or more of the components illustrated in FIG. 3 can be part of thedisplay 220. The various components alone or in combination can collecta variety of data (such as e.g., audio or visual data) associated withthe user of the wearable system 200 or the user's environment. It shouldbe appreciated that other embodiments may have additional or fewercomponents depending on the application for which the wearable system isused. Nevertheless, FIG. 3 provides a basic idea of some of the variouscomponents and types of data that may be collected, analyzed, and storedthrough the wearable system.

FIG. 3 shows an example wearable system 200 which can include thedisplay 220. The display 220 can comprise a display lens 226 that may bemounted to a user's head or a housing or frame 230, which corresponds tothe frame 230. The display lens 226 may comprise one or more transparentmirrors positioned by the housing 230 in front of the user's eyes 302,304 and may be configured to bounce projected light 338 into the eyes302, 304 and facilitate beam shaping, while also allowing fortransmission of at least some light from the local environment. Thewavefront of the projected light beam 338 may be bent or focused tocoincide with a desired focal distance of the projected light. Asillustrated, two wide-field-of-view machine vision cameras 316 (alsoreferred to as world cameras) can be coupled to the housing 230 to imagethe environment around the user. These cameras 316 can be dual capturevisible light/non-visible (e.g., infrared) light cameras. The cameras316 may be part of the outward-facing imaging system 464 shown in FIG.4. Image acquired by the world cameras 316 can be processed by the poseprocessor 336. For example, the pose processor 336 can implement one ormore object recognizers 708 (e.g., shown in FIG. 7) to identify a poseof a user or another person in the user's environment or to identify aphysical object in the user's environment.

With continued reference to FIG. 3, a pair of scanned-lasershaped-wavefront (e.g., for depth) light projector modules with displaymirrors and optics configured to project light 338 into the eyes 302,304 are shown. The depicted view also shows two miniature infraredcameras 324 paired with infrared light (such as light emitting diodes“LED”s), which are configured to be able to track the eyes 302, 304 ofthe user to support rendering and user input. The cameras 324 may bepart of the inward-facing imaging system 462 shown in FIG. 4 Thewearable system 200 can further feature a sensor assembly 339, which maycomprise X, Y, and Z axis accelerometer capability as well as a magneticcompass and X, Y, and Z axis gyro capability, preferably providing dataat a relatively high frequency, such as 200 Hz. The sensor assembly 339may be part of the IMU described with reference to FIG. 2A The depictedsystem 200 can also comprise a head pose processor 336, such as an ASIC(application specific integrated circuit), FPGA (field programmable gatearray), or ARM processor (advanced reduced-instruction-set machine),which may be configured to calculate real or near-real time user headpose from wide field of view image information output from the capturedevices 316. The head pose processor 336 can be a hardware processor andcan be implemented as part of the local processing and data module 260shown in FIG. 2A.

The wearable system can also include one or more depth sensors 234. Thedepth sensor 234 can be configured to measure the distance between anobject in an environment to a wearable device. The depth sensor 234 mayinclude a laser scanner (e.g., a lidar), an ultrasonic depth sensor, ora depth sensing camera. In certain implementations, where the cameras316 have depth sensing ability, the cameras 316 may also be consideredas depth sensors 234.

Also shown is a processor 332 configured to execute digital or analogprocessing to derive pose from the gyro, compass, or accelerometer datafrom the sensor assembly 339. The processor 332 may be part of the localprocessing and data module 260 shown in FIG. 2. The wearable system 200as shown in FIG. 3 can also include a position system such as, e.g., aGPS 337 (global positioning system) to assist with pose and positioninganalyses. In addition, the GPS may further provide remotely-based (e.g.,cloud-based) information about the user's environment. This informationmay be used for recognizing objects or information in user'senvironment.

The wearable system may combine data acquired by the GPS 337 and aremote computing system (such as, e.g., the remote processing module270, another user's ARD, etc.) which can provide more information aboutthe user's environment. As one example, the wearable system candetermine the user's location based on GPS data and retrieve a world map(e.g., by communicating with a remote processing module 270) includingvirtual objects associated with the user's location. As another example,the wearable system 200 can monitor the environment using the worldcameras 316 (which may be part of the outward-facing imaging system 464shown in FIG. 4). Based on the images acquired by the world cameras 316,the wearable system 200 can detect objects in the environment (e.g., byusing one or more object recognizers 708 shown in FIG. 7). The wearablesystem can further use data acquired by the GPS 337 to interpret thecharacters.

The wearable system 200 may also comprise a rendering engine 334 whichcan be configured to provide rendering information that is local to theuser to facilitate operation of the scanners and imaging into the eyesof the user, for the user's view of the world. The rendering engine 334may be implemented by a hardware processor (such as, e.g., a centralprocessing unit or a graphics processing unit). In some embodiments, therendering engine is part of the local processing and data module 260.The rendering engine 334 can be communicatively coupled (e.g., via wiredor wireless links) to other components of the wearable system 200. Forexample, the rendering engine 334, can be coupled to the eye cameras 324via communication link 274, and be coupled to a projecting subsystem 318(which can project light into user's eyes 302, 304 via a scanned laserarrangement in a manner similar to a retinal scanning display) via thecommunication link 272. The rendering engine 334 can also be incommunication with other processing units such as, e.g., the sensor poseprocessor 332 and the image pose processor 336 via links 276 and 294respectively.

The cameras 324 (e.g., mini infrared cameras) may be utilized to trackthe eye pose to support rendering and user input. Some example eye posesmay include where the user is looking or at what depth he or she isfocusing (which may be estimated with eye vergence). The GPS 337, gyros,compass, and accelerometers 339 may be utilized to provide coarse orfast pose estimates. One or more of the cameras 316 can acquire imagesand pose, which in conjunction with data from an associated cloudcomputing resource, may be utilized to map the local environment andshare user views with others.

The example components depicted in FIG. 3 are for illustration purposesonly. Multiple sensors and other functional modules are shown togetherfor ease of illustration and description. Some embodiments may includeonly one or a subset of these sensors or modules. Further, the locationsof these components are not limited to the positions depicted in FIG. 3.Some components may be mounted to or housed within other components,such as a belt-mounted component, a hand-held component, or a helmetcomponent. As one example, the image pose processor 336, sensor poseprocessor 332, and rendering engine 334 may be positioned in a beltpackand configured to communicate with other components of the wearablesystem via wireless communication, such as ultra-wideband, Wi-Fi,Bluetooth, etc., or via wired communication. The depicted housing 230preferably is head-mountable and wearable by the user. However, somecomponents of the wearable system 200 may be worn to other portions ofthe user's body. For example, the speaker 240 may be inserted into theears of a user to provide sound to the user.

Regarding the projection of light 338 into the eyes 302, 304 of theuser, in some embodiment, the cameras 324 may be utilized to measurewhere the centers of a user's eyes are geometrically verged to, which,in general, coincides with a position of focus, or “depth of focus”, ofthe eyes. A 3-dimensional surface of all points the eyes verge to can bereferred to as the “horopter”. The focal distance may take on a finitenumber of depths, or may be infinitely varying. Light projected from thevergence distance appears to be focused to the subject eye 302, 304,while light in front of or behind the vergence distance is blurred.Examples of wearable devices and other display systems of the presentdisclosure are also described in U.S. Patent Publication No.2016/0270656, which is incorporated by reference herein in its entirety.

The human visual system is complicated and providing a realisticperception of depth is challenging. Viewers of an object may perceivethe object as being three-dimensional due to a combination of vergenceand accommodation. Vergence movements (e.g., rolling movements of thepupils toward or away from each other to converge the lines of sight ofthe eyes to fixate upon an object) of the two eyes relative to eachother are closely associated with focusing (or “accommodation”) of thelenses of the eyes. Under normal conditions, changing the focus of thelenses of the eyes, or accommodating the eyes, to change focus from oneobject to another object at a different distance will automaticallycause a matching change in vergence to the same distance, under arelationship known as the “accommodation-vergence reflex.” Likewise, achange in vergence will trigger a matching change in accommodation,under normal conditions. Display systems that provide a better matchbetween accommodation and vergence may form more realistic andcomfortable simulations of three-dimensional imagery.

Further spatially coherent light with a beam diameter of less than about0.7 millimeters can be correctly resolved by the human eye regardless ofwhere the eye focuses. Thus, to create an illusion of proper focaldepth, the eye vergence may be tracked with the cameras 324, and therendering engine 334 and projection subsystem 318 may be utilized torender all objects on or close to the horopter in focus, and all otherobjects at varying degrees of defocus (e.g., using intentionally-createdblurring). Preferably, the system 220 renders to the user at a framerate of about 60 frames per second or greater. As described above,preferably, the cameras 324 may be utilized for eye tracking, andsoftware may be configured to pick up not only vergence geometry butalso focus location cues to serve as user inputs. Preferably, such adisplay system is configured with brightness and contrast suitable forday or night use.

In some embodiments, the display system preferably has latency of lessthan about 20 milliseconds for visual object alignment, less than about0.1 degree of angular alignment, and about 1 arc minute of resolution,which, without being limited by theory, is believed to be approximatelythe limit of the human eye. The display system 220 may be integratedwith a localization system, which may involve GPS elements, opticaltracking, compass, accelerometers, or other data sources, to assist withposition and pose determination; localization information may beutilized to facilitate accurate rendering in the user's view of thepertinent world (e.g., such information would facilitate the glasses toknow where they are with respect to the real world).

In some embodiments, the wearable system 200 is configured to displayone or more virtual images based on the accommodation of the user'seyes. Unlike prior 3D display approaches that force the user to focuswhere the images are being projected, in some embodiments, the wearablesystem is configured to automatically vary the focus of projectedvirtual content to allow for a more comfortable viewing of one or moreimages presented to the user. For example, if the user's eyes have acurrent focus of 1 m, the image may be projected to coincide with theuser's focus. If the user shifts focus to 3 m, the image is projected tocoincide with the new focus. Thus, rather than forcing the user to apredetermined focus, the wearable system 200 of some embodiments allowsthe user's eye to a function in a more natural manner.

Such a wearable system 200 may eliminate or reduce the incidences of eyestrain, headaches, and other physiological symptoms typically observedwith respect to virtual reality devices. To achieve this, variousembodiments of the wearable system 200 are configured to project virtualimages at varying focal distances, through one or more variable focuselements (VFEs). In one or more embodiments, 3D perception may beachieved through a multi-plane focus system that projects images atfixed focal planes away from the user. Other embodiments employ variableplane focus, wherein the focal plane is moved back and forth in thez-direction to coincide with the user's present state of focus.

In both the multi-plane focus systems and variable plane focus systems,wearable system 200 may employ eye tracking to determine a vergence ofthe user's eyes, determine the user's current focus, and project thevirtual image at the determined focus. In other embodiments, wearablesystem 200 comprises a light modulator that variably projects, through afiber scanner, or other light generating source, light beams of varyingfocus in a raster pattern across the retina. Thus, the ability of thedisplay of the wearable system 200 to project images at varying focaldistances not only eases accommodation for the user to view objects in3D, but may also be used to compensate for user ocular anomalies, asfurther described in U.S. Patent Publication No. 2016/0270656, which isincorporated by reference herein in its entirety. In some otherembodiments, a spatial light modulator may project the images to theuser through various optical components. For example, as describedfurther below, the spatial light modulator may project the images ontoone or more waveguides, which then transmit the images to the user.

Waveguide Stack Assembly

FIG. 4 illustrates an example of a waveguide stack for outputting imageinformation to a user. A wearable system 400 includes a stack ofwaveguides, or stacked waveguide assembly 480 that may be utilized toprovide three-dimensional perception to the eye/brain using a pluralityof waveguides 432 b, 434 b, 436 b, 438 b, 4400 b. In some embodiments,the wearable system 400 may correspond to wearable system 200 of FIG. 2,with FIG. 4 schematically showing some parts of that wearable system 200in greater detail. For example, in some embodiments, the waveguideassembly 480 may be integrated into the display 220 of FIG. 2.

With continued reference to FIG. 4, the waveguide assembly 480 may alsoinclude a plurality of features 458, 456, 454, 452 between thewaveguides. In some embodiments, the features 458, 456, 454, 452 may belenses. In other embodiments, the features 458, 456, 454, 452 may not belenses. Rather, they may simply be spacers (e.g., cladding layers orstructures for forming air gaps).

The waveguides 432 b, 434 b, 436 b, 438 b, 440 b or the plurality oflenses 458, 456, 454, 452 may be configured to send image information tothe eye with various levels of wavefront curvature or light raydivergence. Each waveguide level may be associated with a particulardepth plane and may be configured to output image informationcorresponding to that depth plane. Image injection devices 420, 422,424, 426, 428 may be utilized to inject image information into thewaveguides 440 b, 438 b, 436 b, 434 b, 432 b, each of which may beconfigured to distribute incoming light across each respectivewaveguide, for output toward the eye 410. Light exits an output surfaceof the image injection devices 420, 422, 424, 426, 428 and is injectedinto a corresponding input edge of the waveguides 440 b, 438 b, 436 b,434 b, 432 b. In some embodiments, a single beam of light (e.g., acollimated beam) may be injected into each waveguide to output an entirefield of cloned collimated beams that are directed toward the eye 410 atparticular angles (and amounts of divergence) corresponding to the depthplane associated with a particular waveguide.

In some embodiments, the image injection devices 420, 422, 424, 426, 428are discrete displays that each produce image information for injectioninto a corresponding waveguide 440 b, 438 b, 436 b, 434 b, 432 b,respectively. In some other embodiments, the image injection devices420, 422, 424, 426, 428 are the output ends of a single multiplexeddisplay which may, e.g., pipe image information via one or more opticalconduits (such as fiber optic cables) to each of the image injectiondevices 420, 422, 424, 426, 428.

A controller 460 controls the operation of the stacked waveguideassembly 480 and the image injection devices 420, 422, 424, 426, 428.The controller 460 includes programming (e.g., instructions in anon-transitory computer-readable medium) that regulates the timing andprovision of image information to the waveguides 440 b, 438 b, 436 b,434 b, 432 b. In some embodiments, the controller 460 may be a singleintegral device, or a distributed system connected by wired or wirelesscommunication channels. The controller 460 may be part of the processingmodules 260 or 270 (illustrated in FIG. 2) in some embodiments.

The waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be configured topropagate light within each respective waveguide by total internalreflection (TIR). The waveguides 440 b, 438 b, 436 b, 434 b, 432 b mayeach be planar or have another shape (e.g., curved), with major top andbottom surfaces and edges extending between those major top and bottomsurfaces. In the illustrated configuration, the waveguides 440 b, 438 b,436 b, 434 b, 432 b may each include light extracting optical elements440 a, 438 a, 436 a, 434 a, 432 a that are configured to extract lightout of a waveguide by redirecting the light, propagating within eachrespective waveguide, out of the waveguide to output image informationto the eye 410. Extracted light may also be referred to as outcoupledlight, and light extracting optical elements may also be referred to asoutcoupling optical elements. An extracted beam of light is outputted bythe waveguide at locations at which the light propagating in thewaveguide strikes a light redirecting element. The light extractingoptical elements (440 a, 438 a, 436 a, 434 a, 432 a) may, for example,be reflective or diffractive optical features. While illustrateddisposed at the bottom major surfaces of the waveguides 440 b, 438 b,436 b, 434 b, 432 b for ease of description and drawing clarity, in someembodiments, the light extracting optical elements 440 a, 438 a, 436 a,434 a, 432 a may be disposed at the top or bottom major surfaces, or maybe disposed directly in the volume of the waveguides 440 b, 438 b, 436b, 434 b, 432 b. In some embodiments, the light extracting opticalelements 440 a, 438 a, 436 a, 434 a, 432 a may be formed in a layer ofmaterial that is attached to a transparent substrate to form thewaveguides 440 b, 438 b, 436 b, 434 b, 432 b. In some other embodiments,the waveguides 440 b, 438 b, 436 b, 434 b, 432 b may be a monolithicpiece of material and the light extracting optical elements 440 a, 438a, 436 a, 434 a, 432 a may be formed on a surface or in the interior ofthat piece of material.

With continued reference to FIG. 4, as discussed herein, each waveguide440 b, 438 b, 436 b, 434 b, 432 b is configured to output light to forman image corresponding to a particular depth plane. For example, thewaveguide 432 b nearest the eye may be configured to deliver collimatedlight, as injected into such waveguide 432 b, to the eye 410. Thecollimated light may be representative of the optical infinity focalplane. The next waveguide up 434 b may be configured to send outcollimated light which passes through the first lens 452 (e.g., anegative lens) before it can reach the eye 410. First lens 452 may beconfigured to create a slight convex wavefront curvature so that theeye/brain interprets light coming from that next waveguide up 434 b ascoming from a first focal plane closer inward toward the eye 410 fromoptical infinity. Similarly, the third up waveguide 436 b passes itsoutput light through both the first lens 452 and second lens 454 beforereaching the eye 410. The combined optical power of the first and secondlenses 452 and 454 may be configured to create another incrementalamount of wavefront curvature so that the eye/brain interprets lightcoming from the third waveguide 436 b as coming from a second focalplane that is even closer inward toward the person from optical infinitythan was light from the next waveguide up 434 .

The other waveguide layers (e.g., waveguides 438 b, 440 b) and lenses(e.g., lenses 456, 458) are similarly configured, with the highestwaveguide 440 b in the stack sending its output through all of thelenses between it and the eye for an aggregate focal powerrepresentative of the closest focal plane to the person. To compensatefor the stack of lenses 458, 456, 454, 452 when viewing/interpretinglight coming from the world 470 on the other side of the stackedwaveguide assembly 480, a compensating lens layer 430 may be disposed atthe top of the stack to compensate for the aggregate power of the lensstack 458, 456, 454, 452 below. Such a configuration provides as manyperceived focal planes as there are available waveguide/lens pairings.Both the light extracting optical elements of the waveguides and thefocusing aspects of the lenses may be static (e.g., not dynamic orelectro-active). In some alternative embodiments, either or both may bedynamic using electro-active features.

With continued reference to FIG. 4, the light extracting opticalelements 440 a, 438 a, 436 a, 434 a, 432 a may be configured to bothredirect light out of their respective waveguides and to output thislight with the appropriate amount of divergence or collimation for aparticular depth plane associated with the waveguide. As a result,waveguides having different associated depth planes may have differentconfigurations of light extracting optical elements, which output lightwith a different amount of divergence depending on the associated depthplane. In some embodiments, as discussed herein, the light extractingoptical elements 440 a, 438 a, 436 a, 434 a, 432 a may be volumetric orsurface features, which may be configured to output light at specificangles. For example, the light extracting optical elements 440 a, 438 a,436 a, 434 a, 432 a may be volume holograms, surface holograms, and/ordiffraction gratings. Light extracting optical elements, such asdiffraction gratings, are described in U.S. Patent Publication No.2015/0178939, published Jun. 25, 2015, which is incorporated byreference herein in its entirety.

In some embodiments, the light extracting optical elements 440 a, 438 a,436 a, 434 a, 432 a are diffractive features that form a diffractionpattern, or “diffractive optical element” (also referred to herein as a“DOE”). Preferably, the DOE has a relatively low diffraction efficiencyso that only a portion of the light of the beam is deflected away towardthe eye 410 with each intersection of the DOE, while the rest continuesto move through a waveguide via total internal reflection. The lightcarrying the image information can thus be divided into a number ofrelated exit beams that exit the waveguide at a multiplicity oflocations and the result is a fairly uniform pattern of exit emissiontoward the eye 304 for this particular collimated beam bouncing aroundwithin a waveguide.

In some embodiments, one or more DOEs may be switchable between “on”state in which they actively diffract, and “off” state in which they donot significantly diffract. For instance, a switchable DOE may comprisea layer of polymer dispersed liquid crystal, in which microdropletscomprise a diffraction pattern in a host medium, and the refractiveindex of the microdroplets can be switched to substantially match therefractive index of the host material (in which case the pattern doesnot appreciably diffract incident light) or the microdroplet can beswitched to an index that does not match that of the host medium (inwhich case the pattern actively diffracts incident light).

In some embodiments, the number and distribution of depth planes ordepth of field may be varied dynamically based on the pupil sizes ororientations of the eyes of the viewer. Depth of field may changeinversely with a viewer's pupil size. As a result, as the sizes of thepupils of the viewer's eyes decrease, the depth of field increases suchthat one plane that is not discernible because the location of thatplane is beyond the depth of focus of the eye may become discernible andappear more in focus with reduction of pupil size and commensurate withthe increase in depth of field. Likewise, the number of spaced apartdepth planes used to present different images to the viewer may bedecreased with the decreased pupil size. For example, a viewer may notbe able to clearly perceive the details of both a first depth plane anda second depth plane at one pupil size without adjusting theaccommodation of the eye away from one depth plane and to the otherdepth plane. These two depth planes may, however, be sufficiently infocus at the same time to the user at another pupil size withoutchanging accommodation.

In some embodiments, the display system may vary the number ofwaveguides receiving image information based upon determinations ofpupil size or orientation, or upon receiving electrical signalsindicative of particular pupil size or orientation. For example, if theuser's eyes are unable to distinguish between two depth planesassociated with two waveguides, then the controller 460 (which may be anembodiment of the local processing and data module 260) can beconfigured or programmed to cease providing image information to one ofthese waveguides. Advantageously, this may reduce the processing burdenon the system, thereby increasing the responsiveness of the system. Inembodiments in which the DOEs for a waveguide are switchable between theon and off states, the DOEs may be switched to the off state when thewaveguide does receive image information.

In some embodiments, it may be desirable to have an exit beam meet thecondition of having a diameter that is less than the diameter of the eyeof a viewer. However, meeting this condition may be challenging in viewof the variability in size of the viewer's pupils. In some embodiments,this condition is met over a wide range of pupil sizes by varying thesize of the exit beam in response to determinations of the size of theviewer's pupil. For example, as the pupil size decreases, the size ofthe exit beam may also decrease. In some embodiments, the exit beam sizemay be varied using a variable aperture.

The wearable system 400 can include an outward-facing imaging system 464(e.g., a digital camera) that images a portion of the world 470. Thisportion of the world 470 may be referred to as the field of view (FOV)of a world camera and the imaging system 464 is sometimes referred to asan FOV camera. The FOV of the world camera may or may not be the same asthe FOV of a viewer 210 which encompasses a portion of the world 470 theviewer 210 perceives at a given time. For example, in some situations,the FOV of the world camera may be larger than the viewer 210 of theviewer 210 of the wearable system 400. The entire region available forviewing or imaging by a viewer may be referred to as the field of regard(FOR). The FOR may include 4π steradians of solid angle surrounding thewearable system 400 because the wearer can move his body, head, or eyesto perceive substantially any direction in space. In other contexts, thewearer's movements may be more constricted, and accordingly the wearer'sFOR may subtend a smaller solid angle. Images obtained from theoutward-facing imaging system 464 can be used to track gestures made bythe user (e.g., hand or finger gestures), detect objects in the world470 in front of the user, and so forth.

The wearable system 400 can include an audio sensor 232, e.g., amicrophone, to capture ambient sound. As described above, in someembodiments, one or more other audio sensors can be positioned toprovide stereo sound reception useful to the determination of locationof a speech source. The audio sensor 232 can comprise a directionalmicrophone, as another example, which can also provide such usefuldirectional information as to where the audio source is located. Thewearable system 400 can use information from both the outward-facingimaging system 464 and the audio sensor 230 in locating a source ofspeech, or to determine an active speaker at a particular moment intime, etc. For example, the wearable system 400 can use the voicerecognition alone or in combination with a reflected image of thespeaker (e.g., as seen in a mirror) to determine the identity of thespeaker. As another example, the wearable system 400 can determine aposition of the speaker in an environment based on sound acquired fromdirectional microphones. The wearable system 400 can parse the soundcoming from the speaker's position with speech recognition algorithms todetermine the content of the speech and use voice recognition techniquesto determine the identity (e.g., name or other demographic information)of the speaker.

The wearable system 400 can also include an inward-facing imaging system466 (e.g., a digital camera), which observes the movements of the user,such as the eye movements and the facial movements. The inward-facingimaging system 466 may be used to capture images of the eye 410 todetermine the size and/or orientation of the pupil of the eye 304. Theinward-facing imaging system 466 can be used to obtain images for use indetermining the direction the user is looking (e.g., eye pose) or forbiometric identification of the user (e.g., via iris identification). Insome embodiments, at least one camera may be utilized for each eye, toseparately determine the pupil size or eye pose of each eyeindependently, thereby allowing the presentation of image information toeach eye to be dynamically tailored to that eye. In some otherembodiments, the pupil diameter or orientation of only a single eye 410(e.g., using only a single camera per pair of eyes) is determined andassumed to be similar for both eyes of the user. The images obtained bythe inward-facing imaging system 466 may be analyzed to determine theuser's eye pose or mood, which can be used by the wearable system 400 todecide which audio or visual content should be presented to the user.The wearable system 400 may also determine head pose (e.g., headposition or head orientation) using sensors such as IMUs,accelerometers, gyroscopes, etc.

The wearable system 400 can include a user input device 466 by which theuser can input commands to the controller 460 to interact with thewearable system 400. For example, the user input device 466 can includea trackpad, a touchscreen, a joystick, a multiple degree-of-freedom(DOF) controller, a capacitive sensing device, a game controller, akeyboard, a mouse, a directional pad (D-pad), a wand, a haptic device, atotem (e.g., functioning as a virtual user input device), and so forth.A multi-DOF controller can sense user input in some or all possibletranslations (e.g., left/right, forward/backward, or up/down) orrotations (e.g., yaw, pitch, or roll) of the controller. A multi-DOFcontroller which supports the translation movements may be referred toas a 3DOF while a multi-DOF controller which supports the translationsand rotations may be referred to as 6DOF. In some cases, the user mayuse a finger (e.g., a thumb) to press or swipe on a touch-sensitiveinput device to provide input to the wearable system 400 (e.g., toprovide user input to a user interface provided by the wearable system400). The user input device 466 may be held by the user's hand duringthe use of the wearable system 400. The user input device 466 can be inwired or wireless communication with the wearable system 400.

Other Components of the Wearable System

In many implementations, the wearable system may include othercomponents in addition or in alternative to the components of thewearable system described above. The wearable system may, for example,include one or more haptic devices or components. The haptic devices orcomponents may be operable to provide a tactile sensation to a user. Forexample, the haptic devices or components may provide a tactilesensation of pressure or texture when touching virtual content (e.g.,virtual objects, virtual tools, other virtual constructs). The tactilesensation may replicate a feel of a physical object which a virtualobject represents, or may replicate a feel of an imagined object orcharacter (e.g., a dragon) which the virtual content represents. In someimplementations, haptic devices or components may be worn by the user(e.g., a user wearable glove). In some implementations, haptic devicesor components may be held by the user.

The wearable system may, for example, include one or more physicalobjects which are manipulable by the user to allow input or interactionwith the wearable system. These physical objects may be referred toherein as totems. Some totems may take the form of inanimate objects,such as for example, a piece of metal or plastic, a wall, a surface oftable. In certain implementations, the totems may not actually have anyphysical input structures (e.g., keys, triggers, joystick, trackball,rocker switch). Instead, the totem may simply provide a physicalsurface, and the wearable system may render a user interface so as toappear to a user to be on one or more surfaces of the totem. Forexample, the wearable system may render an image of a computer keyboardand trackpad to appear to reside on one or more surfaces of a totem. Forexample, the wearable system may render a virtual computer keyboard andvirtual trackpad to appear on a surface of a thin rectangular plate ofaluminum which serves as a totem. The rectangular plate does not itselfhave any physical keys or trackpad or sensors. However, the wearablesystem may detect user manipulation or interaction or touches with therectangular plate as selections or inputs made via the virtual keyboardor virtual trackpad. The user input device 466 (shown in FIG. 4) may bean embodiment of a totem, which may include a trackpad, a touchpad, atrigger, a joystick, a trackball, a rocker or virtual switch, a mouse, akeyboard, a multi-degree-of-freedom controller, or another physicalinput device. A user may use the totem, alone or in combination withposes, to interact with the wearable system or other users.

Examples of haptic devices and totems usable with the wearable devices,HMD, and display systems of the present disclosure are described in U.S.Patent Publication No. 2015/0016777, which is incorporated by referenceherein in its entirety.

Example Processes of User Interactions with A Wearable System

FIG. 5 is a process flow diagram of an example of a method 500 forinteracting with a virtual user interface. The method 500 may beperformed by the wearable system described herein. Embodiments of themethod 500 can be used by the wearable system to detect persons ordocuments in the FOV of the wearable system.

At block 510, the wearable system may identify a particular UI. The typeof UI may be predetermined by the user. The wearable system may identifythat a particular UI needs to be populated based on a user input (e.g.,gesture, visual data, audio data, sensory data, direct command, etc.).The UI can be specific to a security scenario where the wearer of thesystem is observing users who present documents to the wearer (e.g., ata travel checkpoint). At block 520, the wearable system may generatedata for the virtual UI. For example, data associated with the confines,general structure, shape of the UI etc., may be generated. In addition,the wearable system may determine map coordinates of the user's physicallocation so that the wearable system can display the UI in relation tothe user's physical location. For example, if the UI is body centric,the wearable system may determine the coordinates of the user's physicalstance, head pose, or eye pose such that a ring UI can be displayedaround the user or a planar UI can be displayed on a wall or in front ofthe user. In the security context described herein, the UI may bedisplayed as if the UI were surrounding the traveler who is presentingdocuments to the wearer of the system, so that the wearer can readilyview the UI while looking at the traveler and the traveler's documents.If the UI is hand centric, the map coordinates of the user's hands maybe determined. These map points may be derived through data receivedthrough the FOV cameras, sensory input, or any other type of collecteddata.

At block 530, the wearable system may send the data to the display fromthe cloud or the data may be sent from a local database to the displaycomponents. At block 540, the UI is displayed to the user based on thesent data. For example, a light field display can project the virtual UIinto one or both of the user's eyes. Once the virtual UI has beencreated, the wearable system may simply wait for a command from the userto generate more virtual content on the virtual UI at block 550. Forexample, the UI may be a body centric ring around the user's body or thebody of a person in the user's environment (e.g., a traveler). Thewearable system may then wait for the command (a gesture, a head or eyemovement, voice command, input from a user input device, etc.), and ifit is recognized (block 560), virtual content associated with thecommand may be displayed to the user (block 570).

Examples of Avatar Rendering in Mixed Reality

A wearable system may employ various mapping related techniques in orderto achieve high depth of field in the rendered light fields. In mappingout the virtual world, it is advantageous to know all the features andpoints in the real world to accurately portray virtual objects inrelation to the real world. To this end, FOV images captured from usersof the wearable system can be added to a world model by including newpictures that convey information about various points and features ofthe real world. For example, the wearable system can collect a set ofmap points (such as 2D points or 3D points) and find new map points torender a more accurate version of the world model. The world model of afirst user can be communicated (e.g., over a network such as a cloudnetwork) to a second user so that the second user can experience theworld surrounding the first user.

FIG. 6A is a block diagram of another example of a wearable system whichcan comprise an avatar processing and rendering system 690 in a mixedreality environment. The wearable system 600 may be part of the wearablesystem 200 shown in FIG. 2. In this example, the wearable system 600 cancomprise a map 620, which may include at least a portion of the data inthe map database 710 (shown in FIG. 7). The map may partly residelocally on the wearable system, and may partly reside at networkedstorage locations accessible by wired or wireless network (e.g., in acloud system). A pose process 610 may be executed on the wearablecomputing architecture (e.g., processing module 260 or controller 460)and utilize data from the map 620 to determine position and orientationof the wearable computing hardware or user. Pose data may be computedfrom data collected on the fly as the user is experiencing the systemand operating in the world. The data may comprise images, data fromsensors (such as inertial measurement units, which generally compriseaccelerometer and gyroscope components) and surface informationpertinent to objects in the real or virtual environment.

A sparse point representation may be the output of a simultaneouslocalization and mapping (e.g., SLAM or vSLAM, referring to aconfiguration wherein the input is images/visual only) process. Thesystem can be configured to not only find out where in the world thevarious components are, but what the world is made of. Pose may be abuilding block that achieves many goals, including populating the mapand using the data from the map.

In one embodiment, a sparse point position may not be completelyadequate on its own, and further information may be needed to produce amultifocal AR, VR, or MR experience. Dense representations, generallyreferring to depth map information, may be utilized to fill this gap atleast in part. Such information may be computed from a process referredto as Stereo 640, wherein depth information is determined using atechnique such as triangulation or time-of-flight sensing. Imageinformation and active patterns (such as infrared patterns created usingactive projectors), images acquired from image cameras, or handgestures/totem 650 may serve as input to the Stereo process 640. Asignificant amount of depth map information may be fused together, andsome of this may be summarized with a surface representation. Forexample, mathematically definable surfaces may be efficient (e.g.,relative to a large point cloud) and digestible inputs to otherprocessing devices like game engines. Thus, the output of the stereoprocess (e.g., a depth map) 640 may be combined in the fusion process630. Pose 610 may be an input to this fusion process 630 as well, andthe output of fusion 630 becomes an input to populating the map process620. Sub-surfaces may connect with each other, such as in topographicalmapping, to form larger surfaces, and the map becomes a large hybrid ofpoints and surfaces.

To resolve various aspects in a mixed reality process 660, variousinputs may be utilized. For example, in the embodiment depicted in FIG.6A, Game parameters may be inputs to determine that the user of thesystem is playing a monster battling game with one or more monsters atvarious locations, monsters dying or running away under variousconditions (such as if the user shoots the monster), walls or otherobjects at various locations, and the like. The world map may includeinformation regarding the location of the objects or semanticinformation of the objects (e.g., classifications such as whether theobject is flat or round, horizontal or vertical, a table or a lamp,etc.) and the world map can be another valuable input to mixed reality.Pose relative to the world becomes an input as well and plays a key roleto almost any interactive system.

Controls or inputs from the user are another input to the wearablesystem 600. As described herein, user inputs can include visual input,gestures, totems, audio input, sensory input, etc. In order to movearound or play a game, for example, the user may need to instruct thewearable system 600 regarding what he or she wants to do. Beyond justmoving oneself in space, there are various forms of user controls thatmay be utilized. In one embodiment, a totem (e.g. a user input device),or an object such as a toy gun may be held by the user and tracked bythe system. The system preferably will be configured to know that theuser is holding the item and understand what kind of interaction theuser is having with the item (e.g., if the totem or object is a gun, thesystem may be configured to understand location and orientation, as wellas whether the user is clicking a trigger or other sensed button orelement which may be equipped with a sensor, such as an IMU, which mayassist in determining what is going on, even when such activity is notwithin the field of view of any of the cameras.)

Hand gesture tracking or recognition may also provide input information.The wearable system 600 may be configured to track and interpret handgestures for button presses, for gesturing left or right, stop, grab,hold, etc. For example, in one configuration, the user may want to flipthrough emails or a calendar in a non-gaming environment, or do a “fistbump” with another person or player. The wearable system 600 may beconfigured to leverage a minimum amount of hand gesture, which may ormay not be dynamic. For example, the gestures may be simple staticgestures like open hand for stop, thumbs up for ok, thumbs down for notok; or a hand flip right, or left, or up/down for directional commands.

Eye tracking is another input (e.g., tracking where the user is lookingto control the display technology to render at a specific depth orrange). In one embodiment, vergence of the eyes may be determined usingtriangulation, and then using a vergence/accommodation model developedfor that particular person, accommodation may be determined. Eyetracking can be performed by the eye camera(s) to determine eye gaze(e.g., direction or orientation of one or both eyes). Other techniquescan be used for eye tracking such as, e.g., measurement of electricalpotentials by electrodes placed near the eye(s) (e.g.,electrooculography).

Speech tracking can be another input can be used alone or in combinationwith other inputs (e.g., totem tracking, eye tracking, gesture tracking,etc.). Speech tracking may include speech recognition, voicerecognition, alone or in combination. The system 600 can include anaudio sensor (e.g., a microphone) that receives an audio stream from theenvironment. The system 600 can incorporate voice recognition technologyto determine who is speaking (e.g., whether the speech is from thewearer of the ARD or another person or voice (e.g., a recorded voicetransmitted by a loudspeaker in the environment)) as well as speechrecognition technology to determine what is being said. The local data &processing module 260 or the remote processing module 270 can processthe audio data from the microphone (or audio data in another stream suchas, e.g., a video stream being watched by the user) to identify contentof the speech by applying various speech recognition algorithms, suchas, e.g., hidden Markov models, dynamic time warping (DTW)-based speechrecognitions, neural networks, deep learning algorithms such as deepfeedforward and recurrent neural networks, end-to-end automatic speechrecognitions, machine learning algorithms (described with reference toFIG. 7), or other algorithms that uses acoustic modeling or languagemodeling, etc.

The local data & processing module 260 or the remote processing module270 can also apply voice recognition algorithms which can identify theidentity of the speaker, such as whether the speaker is the user 210 ofthe wearable system 600 or another person with whom the user isconversing. Some example voice recognition algorithms can includefrequency estimation, hidden Markov models, Gaussian mixture models,pattern matching algorithms, neural networks, matrix representation,Vector Quantization, speaker diarisation, decision trees, and dynamictime warping (DTW) technique. Voice recognition techniques can alsoinclude anti-speaker techniques, such as cohort models, and worldmodels. Spectral features may be used in representing speakercharacteristics. The local data & processing module or the remote dataprocessing module 270 can use various machine learning algorithmsdescribed with reference to FIG. 7 to perform the voice recognition.

An implementation of a wearable system can use these user controls orinputs via a UI. UI elements (e.g., controls, popup windows, bubbles,data entry fields, etc.) can be used, for example, to dismiss a displayof information, e.g., graphics or semantic information of an object.

With regard to the camera systems, the example wearable system 600 shownin FIG. 6A can include three pairs of cameras: a relative wide FOV orpassive SLAM pair of cameras arranged to the sides of the user's face, adifferent pair of cameras oriented in front of the user to handle thestereo imaging process 640 and also to capture hand gestures andtotem/object tracking in front of the user's face. The FOV cameras andthe pair of cameras for the stereo process 640 may be a part of theoutward-facing imaging system 464 (shown in FIG. 4). The wearable system600 can include eye tracking cameras (which may be a part of aninward-facing imaging system 462 shown in FIG. 4) oriented toward theeyes of the user in order to triangulate eye vectors and otherinformation. The wearable system 600 may also comprise one or moretextured light projectors (such as infrared (IR) projectors) to injecttexture into a scene.

The wearable system 600 can comprise an avatar processing and renderingsystem 690. The avatar processing and rendering system 690 can beconfigured to generate, update, animate, and render an avatar based oncontextual information. Some or all of the avatar processing andrendering system 690 can be implemented as part of the local processingand data module 260 or the remote processing module 262, 264 alone or incombination. In various embodiments, multiple avatar processing andrendering systems 690 (e.g., as implemented on different wearabledevices) can be used for rendering the virtual avatar 670. For example,a first user's wearable device may be used to determine the first user'sintent, while a second user's wearable device can determine an avatar'scharacteristics and render the avatar of the first user based on theintent received from the first user's wearable device. The first user'swearable device and the second user's wearable device (or other suchwearable devices) can communicate via a network, for example, as will bedescribed with reference to FIGS. 9A and 9B.

FIG. 6B illustrates an example avatar processing and rendering system690. The avatar processing and rendering system 690 can implement themesh transfer techniques described with reference to FIGS. 11-14C. Theexample avatar processing and rendering system 690 can comprise a 3Dmodel processing system 680, a contextual information analysis system688, an avatar autoscaler 692, an intent mapping system 694, an anatomyadjustment system 698, a stimuli response system 696, alone or incombination. The system 690 is intended to illustrate functionalitiesfor avatar processing and rendering and is not intended to be limiting.For example, in certain implementations, one or more of these systemsmay be part of another system. For example, portions of the contextualinformation analysis system 688 may be part of the avatar autoscaler692, intent mapping system 694, stimuli response system 696, or anatomyadjustment system 698, individually or in combination.

The contextual information analysis system 688 can be configured todetermine environment and object information based on one or more devicesensors described with reference to FIGS. 2 and 3. For example, thecontextual information analysis system 688 can analyze environments andobjects (including physical or virtual objects) of a user's environmentor an environment in which the user's avatar is rendered, using imagesacquired by the outward-facing imaging system 464 of the user or theviewer of the user's avatar. The contextual information analysis system688 can analyze such images alone or in combination with a data acquiredfrom location data or world maps (e.g., maps 620, 710, 910) to determinethe location and layout of objects in the environments. The contextualinformation analysis system 688 can also access biological features ofthe user or human in general for animating the virtual avatar 670realistically. For example, the contextual information analysis system688 can generate a discomfort curve which can be applied to the avatarsuch that a portion of the user's avatar's body (e.g., the head) is notat an uncomfortable (or unrealistic) position with respect to the otherportions of the user's body (e.g., the avatar's head is not turned 270degrees). In certain implementations, one or more object recognizers 708(shown in FIG. 7) may be implemented as part of the contextualinformation analysis system 688.

The avatar autoscaler 692, the intent mapping system 694, and thestimuli response system 696, and anatomy adjustment system 698 can beconfigured to determine the avatar's characteristics based on contextualinformation. Some example characteristics of the avatar can include thesize, appearance, position, orientation, movement, pose, expression,etc. The avatar autoscaler 692 can be configured to automatically scalethe avatar such that the user does not have to look at the avatar at anuncomfortable pose. For example, the avatar autoscaler 692 can increaseor decrease the size of the avatar to bring the avatar to the user's eyelevel such that the user does not need to look down at the avatar orlook up at the avatar respectively. The intent mapping system 694 candetermine an intent of a user's interaction and map the intent to anavatar (rather than the exact user interaction) based on the environmentthat the avatar is rendered in. For example, an intent of a first usermay be to communicate with a second user in a telepresence session (see,e.g., FIG. 9B). Typically, two people face each other whencommunicating. The intent mapping system 694 of the first user'swearable system can determine that such a face-to-face intent existsduring the telepresence session and can cause the first user's wearablesystem to render the second user's avatar to be facing the first user.If the second user were to physically turn around, instead of renderingthe second user's avatar in a turned position (which would cause theback of the second user's avatar to be rendered to the first user), thefirst user's intent mapping system 694 can continue to render the secondavatar's face to the first user, which is the inferred intent of thetelepresence session (e.g., face-to-face intent in this example).

The stimuli response system 696 can identify an object of interest inthe environment and determine an avatar's response to the object ofinterest. For example, the stimuli response system 696 can identify asound source in an avatar's environment and automatically turn theavatar to look at the sound source. The stimuli response system 696 canalso determine a threshold termination condition. For example, thestimuli response system 696 can cause the avatar to go back to itsoriginal pose after the sound source disappears or after a period oftime has elapsed.

The anatomy adjustment system 698 can be configured to adjust the user'spose based on biological features. For example, the anatomy adjustmentsystem 698 can be configured to adjust relative positions between theuser's head and the user's torso or between the user's upper body andlower body based on a discomfort curve.

The 3D model processing system 680 can be configured to animate andcause the display 220 to render a virtual avatar 670. The 3D modelprocessing system 680 can include a virtual character processing system682 and a movement processing system 684. The virtual characterprocessing system 682 can be configured to generate and update a 3Dmodel of a user (for creating and animating the virtual avatar). Themovement processing system 684 can be configured to animate the avatar,such as, e.g., by changing the avatar's pose, by moving the avatararound in a user's environment, or by animating the avatar's facialexpressions, etc. As will further be described herein, the virtualavatar can be animated using rigging techniques. In some embodiments, anavatar is represented in two parts: a surface representation (e.g., adeformable mesh) that is used to render the outward appearance of thevirtual avatar and a hierarchical set of interconnected joints (e.g., acore skeleton) for animating the mesh. In some implementations, thevirtual character processing system 682 can be configured to edit orgenerate surface representations, while the movement processing system684 can be used to animate the avatar by moving the avatar, deformingthe mesh, etc. For example, in some implementations, the movementprocessing system 684 performs the matching of a base mesh to a targetmesh described below with reference to FIGS. 11A-14C.

Examples of Mapping a User's Environment

FIG. 7 is a block diagram of an example of an MR environment 700. The MRenvironment 700 may be configured to receive input (e.g., visual input702 from the user's wearable system, stationary input 704 such as roomcameras, sensory input 706 from various sensors, gestures, totems, eyetracking, user input from the user input device 466 etc.) from one ormore user wearable systems (e.g., wearable system 200 or display system220) or stationary room systems (e.g., room cameras, etc.). The wearablesystems can use various sensors (e.g., accelerometers, gyroscopes,temperature sensors, movement sensors, depth sensors, GPS sensors,inward-facing imaging system, outward-facing imaging system, etc.) todetermine the location and various other attributes of the environmentof the user. This information may further be supplemented withinformation from stationary cameras in the room that may provide imagesor various cues from a different point of view. The image data acquiredby the cameras (such as the room cameras and/or the cameras of theoutward-facing imaging system) may be reduced to a set of mappingpoints.

One or more object recognizers 708 can crawl through the received data(e.g., the collection of points) and recognize or map points, tagimages, attach semantic information to objects with the help of a mapdatabase 710. The map database 710 may comprise various points collectedover time and their corresponding objects. The various devices and themap database can be connected to each other through a network (e.g.,LAN, WAN, etc.) to access the cloud.

Based on this information and collection of points in the map database,the object recognizers 708 a to 708 n may recognize objects in anenvironment. For example, the object recognizers can recognize faces,persons, windows, walls, user input devices, televisions, documents(e.g., travel tickets, driver's license, passport as described in thesecurity examples herein), other objects in the user's environment, etc.One or more object recognizers may be specialized for object withcertain characteristics. For example, the object recognizer 708 a may beused to recognizer faces, while another object recognizer may be usedrecognize documents.

The object recognitions may be performed using a variety of computervision techniques. For example, the wearable system can analyze theimages acquired by the outward-facing imaging system 464 (shown in FIG.4) to perform scene reconstruction, event detection, video tracking,object recognition (e.g., persons or documents), object pose estimation,facial recognition (e.g., from a person in the environment or an imageon a document), learning, indexing, motion estimation, or image analysis(e.g., identifying indicia within documents such as photos, signatures,identification information, travel information, etc.), and so forth. Oneor more computer vision algorithms may be used to perform these tasks.Non-limiting examples of computer vision algorithms include:Scale-invariant feature transform (SIFT), speeded up robust features(SURF), oriented FAST and rotated BRIEF (ORB), binary robust invariantscalable keypoints (BRISK), fast retina keypoint (FREAK), Viola-Jonesalgorithm, Eigenfaces approach, Lucas-Kanade algorithm, Horn-Schunkalgorithm, Mean-shift algorithm, visual simultaneous location andmapping (vSLAM) techniques, a sequential Bayesian estimator (e.g.,Kalman filter, extended Kalman filter, etc.), bundle adjustment,Adaptive thresholding (and other thresholding techniques), IterativeClosest Point (ICP), Semi Global Matching (SGM), Semi Global BlockMatching (SGBM), Feature Point Histograms, various machine learningalgorithms (such as e.g., support vector machine, k-nearest neighborsalgorithm, Naive Bayes, neural network (including convolutional or deepneural networks), or other supervised/unsupervised models, etc.), and soforth.

The object recognitions can additionally or alternatively be performedby a variety of machine learning algorithms. Once trained, the machinelearning algorithm can be stored by the HMD. Some examples of machinelearning algorithms can include supervised or non-supervised machinelearning algorithms, including regression algorithms (such as, forexample, Ordinary Least Squares Regression), instance-based algorithms(such as, for example, Learning Vector Quantization), decision treealgorithms (such as, for example, classification and regression trees),Bayesian algorithms (such as, for example, Naive Bayes), clusteringalgorithms (such as, for example, k-means clustering), association rulelearning algorithms (such as, for example, a-priori algorithms),artificial neural network algorithms (such as, for example, Perceptron),deep learning algorithms (such as, for example, Deep Boltzmann Machine,or deep neural network), dimensionality reduction algorithms (such as,for example, Principal Component Analysis), ensemble algorithms (suchas, for example, Stacked Generalization), and/or other machine learningalgorithms. In some embodiments, individual models can be customized forindividual data sets. For example, the wearable device can generate orstore a base model. The base model may be used as a starting point togenerate additional models specific to a data type (e.g., a particularuser in the telepresence session), a data set (e.g., a set of additionalimages obtained of the user in the telepresence session), conditionalsituations, or other variations. In some embodiments, the wearable HMDcan be configured to utilize a plurality of techniques to generatemodels for analysis of the aggregated data. Other techniques may includeusing pre-defined thresholds or data values.

Based on this information and collection of points in the map database,the object recognizers 708 a to 708 n may recognize objects andsupplement objects with semantic information to give life to theobjects. For example, if the object recognizer recognizes a set ofpoints to be a door, the system may attach some semantic information(e.g., the door has a hinge and has a 90 degree movement about thehinge). If the object recognizer recognizes a set of points to be amirror, the system may attach semantic information that the mirror has areflective surface that can reflect images of objects in the room. Thesemantic information can include affordances of the objects as describedherein. For example, the semantic information may include a normal ofthe object. The system can assign a vector whose direction indicates thenormal of the object. Over time the map database grows as the system(which may reside locally or may be accessible through a wirelessnetwork) accumulates more data from the world. Once the objects arerecognized, the information may be transmitted to one or more wearablesystems. For example, the MR environment 700 may include informationabout a scene happening in California. The environment 700 may betransmitted to one or more users in New York. Based on data receivedfrom an FOV camera and other inputs, the object recognizers and othersoftware components can map the points collected from the variousimages, recognize objects etc., such that the scene may be accurately“passed over” to a second user, who may be in a different part of theworld. The environment 700 may also use a topological map forlocalization purposes.

FIG. 8 is a process flow diagram of an example of a method 800 ofrendering virtual content in relation to recognized objects. The method800 describes how a virtual scene may be presented to a user of thewearable system. The user may be geographically remote from the scene.For example, the user may be in New York, but may want to view a scenethat is presently going on in California, or may want to go on a walkwith a friend who resides in California.

At block 810, the wearable system may receive input from the user andother users regarding the environment of the user. This may be achievedthrough various input devices, and knowledge already possessed in themap database. The user's FOV camera, sensors, GPS, eye tracking, etc.,convey information to the system at block 810. The system may determinesparse points based on this information at block 820. The sparse pointsmay be used in determining pose data (e.g., head pose, eye pose, bodypose, or hand gestures) that can be used in displaying and understandingthe orientation and position of various objects in the user'ssurroundings. The object recognizers 708 a-708 n may crawl through thesecollected points and recognize one or more objects using a map databaseat block 830. This information may then be conveyed to the user'sindividual wearable system at block 840, and the desired virtual scenemay be accordingly displayed to the user at block 850. For example, thedesired virtual scene (e.g., user in CA) may be displayed at theappropriate orientation, position, etc., in relation to the variousobjects and other surroundings of the user in New York.

Example Communications among Multiple Wearable Systems

FIG. 9A schematically illustrates an overall system view depictingmultiple user devices interacting with each other. The computingenvironment 900 includes user devices 930 a, 930 b, 930 c. The userdevices 930 a, 930 b, and 930 c can communicate with each other througha network 990. The user devices 930 a-930 c can each include a networkinterface to communicate via the network 990 with a remote computingsystem 920 (which may also include a network interface 971). The network990 may be a LAN, WAN, peer-to-peer network, radio, Bluetooth, or anyother network. The computing environment 900 can also include one ormore remote computing systems 920. The remote computing system 920 mayinclude server computer systems that are clustered and located atdifferent geographic locations. The user devices 930 a, 930 b, and 930 cmay communicate with the remote computing system 920 via the network990.

The remote computing system 920 may include a remote data repository 980which can maintain information about a specific user's physical and/orvirtual worlds. Data storage 980 can store information related to users,users' environment (e.g., world maps of the user's environment), orconfigurations of avatars of the users. The remote data repository maybe an embodiment of the remote data repository 280 shown in FIG. 2. Theremote computing system 920 may also include a remote processing module970. The remote processing module 970 may be an embodiment of the remoteprocessing module 270 shown in FIG. 2. The remote processing module 970may include one or more processors which can communicate with the userdevices (930 a, 930 b, 930 c) and the remote data repository 980. Theprocessors can process information obtained from user devices and othersources. In some implementations, at least a portion of the processingor storage can be provided by the local processing and data module 260(as shown in FIG. 2). The remote computing system 920 may enable a givenuser to share information about the specific user's own physical and/orvirtual worlds with another user.

The user device may be a wearable device (such as an HMD or an ARD), acomputer, a mobile device, or any other devices alone or in combination.For example, the user devices 930 b and 930 c may be an embodiment ofthe wearable system 200 shown in FIG. 2 (or the wearable system 400shown in FIG. 4) which can be configured to present AR/VR/MR content.

One or more of the user devices can be used with the user input device466 shown in FIG. 4. A user device can obtain information about the userand the user's environment (e.g., using the outward-facing imagingsystem 464 shown in FIG. 4). The user device and/or remote computingsystem 1220 can construct, update, and build a collection of images,points and other information using the information obtained from theuser devices. For example, the user device may process raw informationacquired and send the processed information to the remote computingsystem 1220 for further processing. The user device may also send theraw information to the remote computing system 1220 for processing. Theuser device may receive the processed information from the remotecomputing system 1220 and provide final processing before projecting tothe user. The user device may also process the information obtained andpass the processed information to other user devices. The user devicemay communicate with the remote data repository 1280 while processingacquired information. Multiple user devices and/or multiple servercomputer systems may participate in the construction and/or processingof acquired images.

The information on the physical worlds may be developed over time andmay be based on the information collected by different user devices.Models of virtual worlds may also be developed over time and be based onthe inputs of different users. Such information and models can sometimesbe referred to herein as a world map or a world model. As described withreference to FIGS. 6 and 7, information acquired by the user devices maybe used to construct a world map 910. The world map 910 may include atleast a portion of the map 620 described in FIG. 6A. Various objectrecognizers (e.g. 708 a, 708 b, 708 c . . . 708 n) may be used torecognize objects and tag images, as well as to attach semanticinformation to the objects. These object recognizers are also describedin FIG. 7.

The remote data repository 980 can be used to store data and tofacilitate the construction of the world map 910. The user device canconstantly update information about the user's environment and receiveinformation about the world map 910. The world map 910 may be created bythe user or by someone else. As discussed herein, user devices (e.g. 930a, 930 b, 930 c) and remote computing system 920, alone or incombination, may construct and/or update the world map 910. For example,a user device may be in communication with the remote processing module970 and the remote data repository 980. The user device may acquireand/or process information about the user and the user's environment.The remote processing module 970 may be in communication with the remotedata repository 980 and user devices (e.g. 930 a, 930 b, 930 c) toprocess information about the user and the user's environment. Theremote computing system 920 can modify the information acquired by theuser devices (e.g. 930 a, 930 b, 930 c), such as, e.g. selectivelycropping a user's image, modifying the user's background, adding virtualobjects to the user's environment, annotating a user's speech withauxiliary information, etc. The remote computing system 920 can send theprocessed information to the same and/or different user devices.

Examples of a Telepresence Session

FIG. 9B depicts an example where two users of respective wearablesystems are conducting a telepresence session. Two users (named Alice912 and Bob 914 in this example) are shown in this figure. The two usersare wearing their respective wearable devices 902 and 904 which caninclude an HMD described with reference to FIG. 2 (e.g., the displaydevice 220 of the system 200) for representing a virtual avatar of theother user in the telepresence session. The two users can conduct atelepresence session using the wearable device. Note that the verticalline in FIG. 9B separating the two users is intended to illustrate thatAlice 912 and Bob 914 may (but need not) be in two different locationswhile they communicate via telepresence (e.g., Alice may be inside heroffice in Atlanta while Bob is outdoors in Boston).

As described with reference to FIG. 9A, the wearable devices 902 and 904may be in communication with each other or with other user devices andcomputer systems. For example, Alice's wearable device 902 may be incommunication with Bob's wearable device 904, e.g., via the network 990(shown in FIG. 9A). The wearable devices 902 and 904 can track theusers' environments and movements in the environments (e.g., via therespective outward-facing imaging system 464, or one or more locationsensors) and speech (e.g., via the respective audio sensor 232). Thewearable devices 902 and 904 can also track the users' eye movements orgaze based on data acquired by the inward-facing imaging system 462. Insome situations, the wearable device can also capture or track a user'sfacial expressions or other body movements (e.g., arm or leg movements)where a user is near a reflective surface and the outward-facing imagingsystem 464 can obtain reflected images of the user to observe the user'sfacial expressions or other body movements.

A wearable device can use information acquired of a first user and theenvironment to animate a virtual avatar that will be rendered by asecond user's wearable device to create a tangible sense of presence ofthe first user in the second user's environment. For example, thewearable devices 902 and 904, the remote computing system 920, alone orin combination, may process Alice's images or movements for presentationby Bob's wearable device 904 or may process Bob's images or movementsfor presentation by Alice's wearable device 902. As further describedherein, the avatars can be rendered based on contextual information suchas, e.g., a user's intent, an environment of the user or an environmentin which the avatar is rendered, or other biological features of ahuman.

Although the examples only refer to two users, the techniques describedherein should not be limited to two users. Multiple users (e.g., two,three, four, five, six, or more) using wearables (or other telepresencedevices) may participate in a telepresence session. A particular user'swearable device can present to that particular user the avatars of theother users during the telepresence session. Further, while the examplesin this figure show users as standing in an environment, the users arenot required to stand. Any of the users may stand, sit, kneel, lie down,walk or run, or be in any position or movement during a telepresencesession. The user may also be in a physical environment other thandescribed in examples herein. The users may be in separate environmentsor may be in the same environment while conducting the telepresencesession. Not all users are required to wear their respective HMDs in thetelepresence session. For example, Alice 912 may use other imageacquisition and display devices such as a webcam and computer screenwhile Bob 914 wears the wearable device 904.

Examples of a Virtual Avatar

FIG. 10 illustrates an example of an avatar as perceived by a user of awearable system. The example avatar 1000 shown in FIG. 10 can be anavatar of Alice 912 (shown in FIG. 9B) standing behind a physical plantin a room. An avatar can include various characteristics, such as size,appearance (e.g., skin color, complexion, hair style, clothes, and skinand facial features such as wrinkles, moles, blemishes, pimples,dimples, etc.), position, orientation, movement, pose, expression, etc.These characteristics may be based on the user associated with theavatar (e.g., the avatar 1000 of Alice may have some or allcharacteristics of the actual person Alice 912). As further describedherein, the avatar 1000 can be animated based on contextual information,which can include adjustments to one or more of the characteristics ofthe avatar 1000. Although generally described herein as representing thephysical appearance of the person (e.g., Alice), this is forillustration and not limitation. Alice's avatar could represent theappearance of another real or fictional human being besides Alice, apersonified object, a creature, or any other real or fictitiousrepresentation. Further, the plant in FIG. 10 need not be physical, butcould be a virtual representation of a plant that is presented to theuser by the wearable system. Also, additional or different virtualcontent than shown in FIG. 10 could be presented to the user.

Examples of Rigging Systems for Virtual Characters

An animated virtual character, such as a human avatar, can be wholly orpartially represented in computer graphics as a polygon mesh. A polygonmesh, or simply “mesh” for short, is a collection of points in a modeledthree-dimensional space. The mesh can form a polyhedral object whosesurfaces define the body or shape of the virtual character (or a portionthereof). While meshes can include any number of points (withinpractical limits which may be imposed by available computing power),finer meshes with more points are generally able to portray morerealistic virtual characters with finer details that may closelyapproximate real life people, animals, objects, etc. FIG. 10 shows anexample of a mesh 1010 around an eye of the avatar 1000.

Each point in the mesh can be defined by a coordinate in the modeledthree-dimensional space. The modeled three-dimensional space can be, forexample, a Cartesian space addressed by (x, y, z) coordinates. Thepoints in the mesh are the vertices of the polygons which make up thepolyhedral object. Each polygon represents a surface, or face, of thepolyhedral object and is defined by an ordered set of vertices, with thesides of each polygon being straight line edges connecting the orderedset of vertices. In some cases, the polygon vertices in a mesh maydiffer from geometric polygons in that they are not necessarily coplanarin 3D graphics. In addition, the vertices of a polygon in a mesh may becollinear, in which case the polygon has zero area (referred to as adegenerate polygon).

In some embodiments, a mesh is made up of three-vertex polygons (i.e.,triangles or “tris” for short) or four-vertex polygons (i.e.,quadrilaterals or “quads” for short). However, higher-order polygons canalso be used in some meshes. Meshes are typically quad-based in directcontent creation (DCC) applications (e.g., applications such as Maya(available from Autodesk, Inc.) or Houdini (available from Side EffectsSoftware Inc.) which are primarily designed for creating andmanipulating 3D computer graphics), whereas meshes are typicallytri-based in real-time applications.

To animate a virtual character, its mesh can be deformed by moving someor all of its vertices to new positions in space at various instants intime. The deformations can represent both large-scale movements (e.g.,movement of limbs) and fine movements (e.g., facial movements). Theseand other deformations can be based on real-world models (e.g.,photogrammetric scans of real humans performing body movements,articulations, facial contortions, expressions, etc.), art-directeddevelopment (which may be based on real-world sampling), combinations ofthe same, or other techniques. In the early days of computer graphics,mesh deformations could be accomplished manually by independentlysetting new positions for the vertices, but given the size andcomplexity of modern meshes it is typically desirable to producedeformations using automated systems and processes. The control systems,processes, and techniques for producing these deformations are referredto as rigging, or simply “the rig.” The example avatar processing andrendering system 690 of FIG. 6B includes a 3D model processing system680 which can implement rigging.

The rigging for a virtual character can use skeletal systems to assistwith mesh deformations. A skeletal system includes a collection ofjoints which correspond to points of articulation for the mesh. In thecontext of rigging, joints are sometimes also referred to as “bones”despite the difference between these terms when used in the anatomicalsense. Joints in a skeletal system can move, or otherwise change, withrespect to one another according to transforms which can be applied tothe joints. The transforms can include translations or rotations inspace, as well as other operations. The joints can be assignedhierarchical relationships (e.g., parent-child relationships) withrespect to one another. These hierarchical relationships can allow onejoint to inherit transforms or other characteristics from another joint.For example, a child joint in a skeletal system can inherit a transformassigned to its parent joint so as to cause the child joint to movetogether with the parent joint.

A skeletal system for a virtual character can be defined with joints atappropriate positions, and with appropriate local axes of rotation,degrees of freedom, etc., to allow for a desired set of meshdeformations to be carried out. Once a skeletal system has been definedfor a virtual character, each joint can be assigned, in a process called“skinning,” an amount of influence over the various vertices in themesh. This can be done by assigning a weight value to each vertex foreach joint in the skeletal system. When a transform is applied to anygiven joint, the vertices under its influence can be moved, or otherwisealtered, automatically based on that joint transform by amounts whichcan be dependent upon their respective weight values.

A rig can include multiple skeletal systems. One type of skeletal systemis a core skeleton (also referred to as a low-order skeleton) which canbe used to control large-scale movements of the virtual character. Inthe case of a human avatar, for example, the core skeleton mightresemble the anatomical skeleton of a human. Although the core skeletonfor rigging purposes may not map exactly to an anatomically-correctskeleton, it may have a sub-set of joints in analogous locations withanalogous orientations and movement properties.

As briefly mentioned above, a skeletal system of joints can behierarchical with, for example, parent-child relationships among joints.When a transform (e.g., a change in position and/or orientation) isapplied to a particular joint in the skeletal system, the same transformcan be applied to all other lower-level joints within the samehierarchy. In the case of a rig for a human avatar, for example, thecore skeleton may include separate joints for the avatar's shoulder,elbow, and wrist. Among these, the shoulder joint may be assigned to thehighest level in the hierarchy, while the elbow joint can be assigned asa child of the shoulder joint, and the wrist joint can be assigned as achild of the elbow joint. Accordingly, when a particular translationand/or rotation transform is applied to the shoulder joint, the sametransform can also be applied to the elbow joint and the wrist jointsuch that they are translated and/or rotated in the same way as theshoulder.

Despite the connotations of its name, a skeletal system in a rig neednot necessarily represent an anatomical skeleton. In rigging, skeletalsystems can represent a wide variety of hierarchies used to controldeformations of the mesh. For example, hair can be represented as aseries of joints in a hierarchical chain; skin motions due to anavatar's facial contortions (which may represent expressions such assmiling, frowning, laughing, speaking, blinking, etc.) can berepresented by a series of facial joints controlled by a facial rig;muscle deformation can be modeled by joints; and motion of clothing canbe represented by a grid of joints.

The rig for a virtual character can include multiple skeletal systems,some of which may drive the movement of others. A lower-order skeletalsystem is one which drives one or more higher-order skeletal systems.Conversely, higher-order skeletal systems are ones which are driven orcontrolled by a lower-order skeletal system. For example, whereas themovements of the core skeleton of a character might be controlledmanually by an animator, the core skeleton can in turn drive or controlthe movements of a higher-order skeletal system. For example,higher-order helper joints—which may not have anatomical analogs in aphysical skeleton—can be provided to improve the mesh deformations whichresult from movements of the core skeleton. The transforms applied tothese and other joints in higher-order skeletal systems may be derivedalgorithmically from the transforms applied to the lower-order skeleton.Higher-order skeletons can represent, for example, muscles, skin, fat,clothing, hair, or any other skeletal system which does not requiredirect animation control.

As already discussed, transforms can be applied to joints in skeletalsystems in order to carry out mesh deformations. In the context ofrigging, transforms include functions which accept one or more givenpoints in 3D space and produce an output of one or more new 3D points.For example, a transform can accept one or more 3D points which define ajoint and can output one or more new 3D points which specify thetransformed joint. Joint transforms can include, for example, atranslation component, a rotation component, and a scale component.

A translation is a transform which moves a set of one or more specifiedpoints in the modeled 3D space by a specified amount with no change inthe orientation or size of the set of points. A rotation is a transformwhich rotates a set of one or more specified points in the modeled 3Dspace about a specified axis by a specified amount (e.g., rotate everypoint in the mesh 45 degrees about the z-axis). An affine transform (or6 degree of freedom (DOF) transform) is one which includestranslation(s) and rotation(s). An example application of an affinetransform can be thought of as moving a set of one or more points inspace without changing its size, though the orientation can change.Affine transformations can also include shear or scale changes.

Meanwhile, a scale transform is one which modifies one or more specifiedpoints in the modeled 3D space by scaling their respective coordinatesby a specified value. This changes the size and/or shape of thetransformed set of points. A uniform scale transform scales eachcoordinate by the same amount, whereas a non-uniform scale transform canscale the (x, y, z) coordinates of the specified points independently. Anon-uniform scale transform can be used, for example, to providesquashing and stretching effects, such as those which may result frommuscular action. Yet another type of transform is a shear transform. Ashear transform is one which modifies a set of one or more specifiedpoints in the modeled 3D space by translating a coordinate of the pointsby different amounts based on the distance of that coordinate from anaxis.

When a transform is applied to a joint to cause it to move, the verticesunder the influence of that joint are also moved. This results indeformations of the mesh. As discussed above, the process of assigningweights to quantify the influence each joint has over each vertex iscalled skinning (or sometimes “weight painting” or “skin weighting”).The weights are typically values between 0 (meaning no influence) and 1(meaning complete influence). Some vertices in the mesh may beinfluenced only by a single joint. In that case those vertices areassigned weight values of 1 for that joint, and their positions arechanged based on transforms assigned to that specific joint but noothers. Other vertices in the mesh may be influenced by multiple joints.In that case, separate weights are assigned to those vertices for all ofthe influencing joints, with the sum of the weights for each vertexequaling 1. The positions of these vertices are changed based ontransforms assigned to all of their influencing joints.

Making weight assignments for all of the vertices in a mesh can beextremely labor intensive, especially as the number of joints increases.Balancing the weights to achieve desired mesh deformations in responseto transforms applied to the joints can be quite difficult for evenhighly trained artists. In the case of real-time applications, the taskcan be complicated further by the fact that many real-time systems alsoenforce limits on the number of joints (generally 8 or fewer) which canbe weighted to a specific vertex. Such limits are typically imposed forthe sake of efficiency in the graphics processing unit (GPU).

The term skinning also refers to the process of actually deforming themesh, using the assigned weights, based on transforms applied to thejoints in a skeletal system. For example, a series of core skeletonjoint transforms may be specified by an animator to produce a desiredcharacter movement (e.g., a running movement or a dance step). Whentransforms are applied to one or more of the joints, new positions arecalculated for the vertices under the influence of the transformedjoints. The new position for any given vertex is typically computed as aweighted average of all the joint transforms which influence thatparticular vertex. There are many algorithms used for computing thisweighted average, but the most common, and the one used in mostreal-time applications due to its simplicity and ease of control, islinear blend skinning (LBS). In linear blend skinning, a new positionfor each vertex is calculated using each joint transform for which thatvertex has a non-zero weight. Then, the new vertex coordinates resultingfrom each of these joint transforms are averaged in proportion to therespective weights assigned to that vertex for each of the joints. Thereare well known limitations to LBS in practice, and much of the work inmaking high-quality rigs is devoted to finding and overcoming theselimitations. Many helper joint systems are designed specifically forthis purpose.

In addition to skeletal systems, “blendshapes” can also be used inrigging to produce mesh deformations. A blendshape (sometimes alsocalled a “morph target” or just a “shape”) is a deformation applied to aset of vertices in the mesh where each vertex in the set is moved aspecified amount in a specified direction based upon a weight. Eachvertex in the set may have its own custom motion for a specificblendshape, and moving the vertices in the set simultaneously willgenerate the desired shape. The custom motion for each vertex in ablendshape can be specified by a “delta,” which is a vector representingthe amount and direction of XYZ motion applied to that vertex.Blendshapes can be used to produce, for example, facial deformations tomove the eyes, lips, brows, nose, dimples, etc., just to name a fewpossibilities.

Blendshapes are useful for deforming the mesh in an art-directable way.They offer a great deal of control, as the exact shape can be sculptedor captured from a scan of a model. But the benefits of blendshapes comeat the cost of having to store the deltas for all the vertices in theblendshape. For animated characters with fine meshes and manyblendshapes, the amount of delta data can be significant.

Each blendshape can be applied to a specified degree by using blendshapeweights. These weights typically range from 0 (where the blendshape isnot applied at all) to 1 (where the blendshape is fully active). Forexample, a blendshape to move a character's eyes can be applied with asmall weight to move the eyes a small amount, or it can be applied witha large weight to create a larger eye movement.

The rig may apply multiple blendshapes in combination with one anotherto achieve a desired complex deformation. For example, to produce asmile, the rig may apply, in combination, blendshapes for lip cornerpull, raising the upper lip, and lowering the lower lip, as well asmoving the eyes, brows, nose, and dimples. The desired shape fromcombining two or more blendshapes is known as a combination shape (orsimply a “combo”).

One problem that can result from applying two blendshapes in combinationis that the blendshapes may operate on some of the same vertices. Whenboth blendshapes are active, the result is called a double transform or“going off-model.” The solution to this is typically a correctiveblendshape. A corrective blendshape is a special blendshape whichrepresents a desired deformation with respect to a currently applieddeformation rather than representing a desired deformation with respectto the neutral. Corrective blendshapes (or just “correctives”) can beapplied based upon the weights of the blendshapes they are correcting.For example, the weight for the corrective blendshape can be madeproportionate to the weights of the underlying blendshapes which triggerapplication of the corrective blendshape.

Corrective blendshapes can also be used to correct skinning anomalies orto improve the quality of a deformation. For example, a joint mayrepresent the motion of a specific muscle, but as a single transform itcannot represent all the non-linear behaviors of the skin, fat, andmuscle. Applying a corrective, or a series of correctives, as the muscleactivates can result in more pleasing and convincing deformations.

Rigs are built in layers, with lower, simpler layers often drivinghigher-order layers. This applies to both skeletal systems andblendshape deformations. For example, as already mentioned, the riggingfor an animated virtual character may include higher-order skeletalsystems which are controlled by lower-order skeletal systems. There aremany ways to control a higher-order skeleton or a blendshape based upona lower-order skeleton, including constraints, logic systems, andpose-based deformation.

A constraint is typically a system where a particular object or jointtransform controls one or more components of a transform applied toanother joint or object. There are many different types of constraints.For example, aim constraints change the rotation of the target transformto point in specific directions or at specific objects. Parentconstraints act as virtual parent-child relationships between pairs oftransforms. Position constraints constrain a transform to specificpoints or a specific object. Orientation constraints constrain atransform to a specific rotation of an object.

Logic systems are systems of mathematical equations which produce someoutputs given a set of inputs. These are specified, not learned. Forexample, a blendshape value might be defined as the product of two otherblendshapes (this is an example of a corrective shape known as acombination or combo shape).

Pose-based deformations can also be used to control higher-orderskeletal systems or blendshapes. The pose of a skeletal system isdefined by the collection of transforms (e.g., rotation(s) andtranslation(s)) for all the joints in that skeletal system. Poses canalso be defined for subsets of the joints in a skeletal system. Forexample, an arm pose could be defined by the transforms applied to theshoulder, elbow, and wrist joints. A pose space deformer (PSD) is asystem used to determine a deformation output for a particular posebased on one or more “distances” between that pose and a defined pose.These distances can be metrics which characterize how different one ofthe poses is from the other. A PSD can include a pose interpolation nodewhich, for example, accepts a set of joint rotations (defining a pose)as input parameters and in turn outputs normalized per-pose weights todrive a deformer, such as a blendshape. The pose interpolation node canbe implemented in a variety of ways, including with radial basisfunctions (RBFs). RBFs can perform a machine-learned mathematicalapproximation of a function. RBFs can be trained using a set of inputsand their associated expected outputs. The training data could be, forexample, multiple sets of joint transforms (which define particularposes) and the corresponding blendshapes to be applied in response tothose poses. Once the function is learned, new inputs (e.g., poses) canbe given and their expected outputs can be computed efficiently. RBFsare a subtype of artificial neural networks. RBFs can be used to drivehigher-level components of a rig based upon the state of lower-levelcomponents. For example, the pose of a core skeleton can drive helperjoints and correctives at higher levels.

These control systems can be chained together to perform complexbehaviors. As an example, an eye rig could contain two “look around”values for horizontal and vertical rotation. These values can be passedthrough some logic to determine the exact rotation of an eye jointtransform, which might in turn be used as an input to an RBF whichcontrols blendshapes that change the shape of the eyelid to match theposition of the eye. The activation values of these shapes might be usedto drive other components of a facial expression using additional logic,and so on.

The goal of rigging systems is typically to provide a mechanism toproduce pleasing, high-fidelity deformations based on simple,human-understandable control systems. In the case of real-timeapplications, the goal is typically to provide rigging systems which aresimple enough to run in real-time on, for example, a VR/AR/MR system200, while making as few compromises to the final quality as possible.In some embodiments, the 3D model processing system 680 executes arigging system to animate an avatar in a mixed reality environment 100in real-time to be interactive (with users of the VR/AR/MR system) andto provide appropriate, contextual avatar behavior (e.g., intent-basedbehavior) in the user's environment.

Matching Base Meshes to Target Meshes

As discussed herein, rigging includes techniques for transferringinformation about deformation of the body of an avatar (e.g., facialcontortions, arm movements, leg movements, torso movements, headrotations and tilts, hand movements including grasping and pointing,etc.) onto a mesh. Animating a mesh (e.g., animating an avatar) mayinclude deforming a mesh by moving some or all of the vertices that formthe mesh to new positions in 3D space. The example avatar processing andrendering system 690 of FIG. 6B includes a 3D model processing system680, which can implement a rig (e.g., a control system for deforming themesh and animating an avatar). As a mesh may include large numbers ofvertices, rigs typically provide common, desirable deformations ascomputerized commands that make it easier to control the mesh. Forhigh-end visual effects productions such as movies, there may besufficient production time for rigs to perform massive mathematicalcomputations to achieve highly realistic animation effects—in otherwords, this context typically prioritizes high-quality at the expense ofhigh-speed. But for real-time applications (such as in mixed reality),deformation speed can be very advantageous and different riggingtechniques may be used—in other words, this context typicallyprioritizes high-speed at the expense of high-quality. Rigs oftenutilize deformations that rely on skeletal systems and/or blendshapes.

When making a partial or full body digital avatar, a large number offacial and/or body scans may be acquired as reference for avatar riganimations and deformations. A rig may include a base mesh, which mayrepresent a neutral rest state of the body, that can be deformed by askeletal structure of hierarchical matrices (such as a skin cluster) andusing blendshapes. Scans of facial expressions and body poses may beused as references for blendshape deformations of the rig. Thesereference scans are usually unstructured geometry that are not coherentwith a base mesh. The process of matching the form and shape of thereference scans to one or more base meshes previously required laboriousamounts of hand sculpting vertices. As an example, individual referenceor base scans would have to be manually sculpted into a target mesh forevery single static rig pose (e.g., a straight arm pose, an arm bent at45 degrees pose, an arm fully bent pose, etc.).

The present disclosure overcomes the challenges of laboriously matchingreference scans to one or more base meshes using novel combinations ofrigid transformations, non-rigid deformations, and iterations of suchtransformations and deformations, to procedurally match the shape andform of a base mesh to a scanned reference with minimal humanintervention. In embodiments of the present disclosure, a system isprovided that obtains a base mesh and a target mesh as inputs,procedurally matches the base mesh to the target mesh in a suitablemanner, and outputs a matched base mesh (e.g., a transfer mesh or ablendshape that can be used to assist in animation of an avatar). In atleast some embodiments, avatar processing and rendering system 690 ofFIG. 6B may implement the techniques described herein for matching abase mesh to a target mesh.

As discussed herein in connection with FIG. 10, a mesh, or a polygonmesh, is a collection of points in a modeled three-dimensional space.The mesh can form a polyhedral object whose surfaces define the body orshape of the virtual character (or a portion thereof). While meshes caninclude any number of points (within practical limits which may beimposed by available computing power), finer meshes with more points aregenerally able to portray more realistic virtual characters with finerdetails that may closely approximate real life people, animals, objects,etc. FIG. 10 shows an example of a mesh 1010 around an eye of the avatar1000. The mesh 1010 may be relatively fine (e.g., have a relativelylarge number of points), to facilitate a high quality simulation of thecomplex curves and movements that occur in the eye region.

Examples of Matching Target Meshes to Base Meshes

FIGS. 11A-11B illustrate various examples, including an arm and a shirt,of base and target meshes that may be obtained or provided to thematching system of the present disclosure and transfer meshes (e.g.,matched base meshes) that may be generated or provided by the matchingsystem. Note that in FIGS. 11A-11B the vertices of the mesh are notshown. Base meshes may be obtained by deformation of the skeletalstructure of a rig, from its neutral rest state into a desired pose.Target meshes may be obtained by reference scans of a person in thedesired pose(s). In some embodiments, the reference scans may be 2D or3D photographs or scans of a model. In other embodiments, the referencescans may be 2D or 3D photographs or scans of a user of an electronicdevice such as an AR, MR, or VR device. In such embodiments, the usermay be posing in desired pose(s) at the direction of the electronicdevice (e.g., the electronic device may prompt the user to move intopredetermined poses) or the electronic device may be tracking the userin real-time and animating an avatar to match the user's movements.These are merely illustrative examples and, in general, base, target,and transfer meshes may be obtained for any desired purposes.

In at least some embodiments, a base mesh may refer to a clean mesh or aset of structured vertices, edges, and faces that define a polyhedralobject such as an avatar or other virtual object. Additionally, a targetmesh may refer to a scan mesh or a set of unstructured vertices, edges,and faces that define a polyhedral object such as a model, a user, or anobject. In some arrangements, clean or base meshes may be generated bydeformation of a rig, while scan or target meshes may be generated orobtained using photogrammetry (e.g., the use of photography and othersensors in surveying and mapping to measure distances between objects;such as the 3D mapping of a person or object using cameras and othersensors).

As shown in base mesh 1110 of FIG. 11A, a rig may be deformed from aneutral rest state (which may be a state in which the rig's arm isextended in a relatively straight manner in this example) into a bentarm statement in which the person's left elbow is fully or nearly fullybent. As shown in base mesh 1110, a deformation of the rig into thisposition may not match a user's expected deformation. As an example, inthe region 1112, the rig's forearm and upper arm skin may compress in amanner that appears unnatural (e.g., as a crease in the skin). Incontrast and as shown in target mesh 1120 of FIG. 11A, a reference scanof a model or user in the same pose as the base mesh 1110 may have amore natural appearance in the elbow region 1122. In particular, theforearm may compress the bicep in target mesh 1122, rather than thebicep seemingly disappearing into the forearm as in base mesh 1112.

Using the systems and processes disclosed herein, the base mesh may beaccurately conformed to the target mesh. In some of these systems andprocesses, a heatmap 1130 may be used to identify regions of the basemesh (or target mesh) where there are errors (e.g., deviations betweenthe spatial positions of vertices in the meshes) of differentmagnitudes. As an example, present techniques for matching a base meshto a target mesh may identify regions of relatively high error, such asregions 1132 (shown with single-line hatching), regions of relativelymoderate error, such as regions 1134 (shown with double-line hatching),and regions of relatively low error, such as regions 1136 (shown withcross-hatching and, in portions of the hand having particularly lowerror, without hatching), where error relates to the difference inposition of a given vertex in the base mesh relative to the position ofthe corresponding vertex in the target mesh. As shown in FIG. 11A, thebase mesh may have relatively high positional differences (e.g., higherror) from the target mesh in the regions where the forearm and upperarm muscles compress against each other. Additionally, the base mesh mayhave slightly lower positional differences (e.g., medium error) in theregions of the forearm and upper arm near the high error regions and mayhave little to no positional differences (e.g., no or low error) inregions away from the elbow, such as the hand and shoulder.

The examples 1130, 1160 of FIGS. 11A and 11B may be referred to asheatmaps, with errors between the base and target meshes illustratedvisually. As one example, regions of relatively low or no error may bedenoted by blue coloring (or a certain type of marking such as thecross-hatching of FIGS. 11A and 11B), regions of relatively moderateerror may be denoted by green coloring (or another type of marking suchas the double-line hatching of FIGS. 11A and 11B), and regions ofrelatively high error may be denoted by red coloring (or yet anothertype of marking such as the single-line hatching of FIGS. 11A and 11B).For example, the region 1112 where the elbow shows an unnatural creaseis in the high error, single-hatched region 1132 in the heatmap 1130.

Error, in these examples, may refer to three-dimensional distancesbetween corresponding pairs of vertices between the base and targetmeshes. In some examples, high error regions may have distance errors inexcess of less than 0.2 cm, approximately 0.2 cm, 0.4 cm, 0.6 cm, 0.8cm, 1.0 cm, 2.0 cm, 4.0 cm or more; low to no error regions may havedistance errors of less than approximately 0.05 cm, 0.1 cm, 0.2 cm, 0.3cm, 0.5 cm, 1.0 cm, or more; and moderate error regions may havedistance errors between the high and low error regions (e.g., moderateerror regions may have distance errors that are less than the high errorregions such as distance errors of less than 0.2 cm, 0.4 cm, 0.6 cm, 0.8cm, 1.0 cm, 2.0 cm, or 4.0 cm or more, but that are more than the lowerror regions such as distance errors of more than approximately 0.5 cm,0.1 cm, 0.2 cm, 0.3 cm, 0.5 cm, 1.0 cm, or more). As one example, thehigh error regions may have distance errors in excess of approximately0.4 cm, the moderate error regions may have distance errors betweenapproximately 0.4 cm and approximately 0.1 cm, and the low to no errorregions may have distance errors less than approximately 0.1 cm. Theseare merely illustrative examples. Colors, or other suitable visualindicators, may be utilized to enable quick visual monitoring by humananimators of the disclosed processes for transferring base meshes ontotarget meshes.

Although the heatmaps shown in FIGS. 11A and 11B illustrate three errorregions using different styles of hatching, this is for illustration andis not intended to be limiting. Any number of error regions can be used(e.g., 2, 3, 4, 5, 10, 20, or more). Further, the hatching is intendedto provide a clear visual indication of where the errors occur. In somearrangements, different error regions may be illustrated via coloring(similar to a heatmap showing weather temperatures). In a fullyautomated system, the system would not need to apply hatching (or othergraphical indicia such as colors or other suitable patterns or visualindicators) to the different error regions. However, some systems maygenerate and render a graphical representation of the heatmap to displayto human animators where errors of differing amounts occur. In otherembodiments, additionally or alternatively to a heatmap, other graphicalrepresentations of the error may be generated such as, e.g., a contourplot or any other available graphic techniques. Additionally, althoughthe different error regions have been described as red, green, and blue(from high, to medium, to low errors), any colors could be used, or anytype of quantitative or qualitative scale (e.g., a numerical scale or agrade scale (e.g., A, B, C)) could be used.

As will be further described below, different mesh transfer or meshmatching techniques can be used in different error regions to registerand conform the base mesh onto the target mesh. Because any particularmesh transfer technique will have its own unique strengths andweaknesses, the system advantageously can use an appropriate techniquefor each error region. For example, a rigid mesh transfer technique mayperform well to register the base mesh and the target mesh in regions ofrelatively high error but perform less well to conform the two meshes toeach other in regions of relatively low error. Conversely, a non-rigidmesh transfer technique may perform less well in regions of relativelyhigh error, but perform well to conform the base mesh to the target meshin regions of relatively low error. Accordingly, an implementation ofthe mesh transfer system uses a rigid transfer technique in high errorregions (e.g., single-line hatched regions 1132 in the heatmap 1130), anon-rigid transfer technique in medium error regions (e.g., double-linehatched regions 1134 in the heatmap 1130), and no additional transfer isperformed in low error regions (e.g., cross-hatched regions 1136 in theheatmap 1130) where the two meshes are substantially the same. Thus, thesystem may advantageously apply the appropriate mesh transfer techniqueto each type of region in the heatmap. Further, the system may iteratethis approach to progressively match the base and target meshes untilall (or substantially all) the regions of the mesh are in the low error(e.g., cross-hatched or blue) region of the heatmap.

FIG. 11B illustrates a second example of matching a base mesh to atarget mesh to obtain a transfer mesh. In particular, FIG. 11Billustrates a base mesh 1140 of a shirt, which may be modeled asclothing on an avatar, a target mesh 1150, and a heatmap 1160. The basemesh 1140 may represent the results of deforming a rig of the shirt froma neutral pose into the illustrated pose (e.g., a pose in which a lowerportion of the shirt is raised, such as due to the underlying avatar'sleg being lifted causing the illustrated deformation of the shirt). Asshown in FIG. 11B, the error between the base and target meshes may bemost pronounced in the region where the avatar or user's left leg islifting the shirt. As an example, base mesh 1140 may have a region 1142where the user's leg does not lift the shirt in a natural manner,whereas target mesh 1150 may illustrate a natural curve to the shirt inregion 1152. The target mesh 1150 may represent a scanned reference of ashirt deformed into the illustrated pose (e.g., a scan of a model or auser wearing the shirt and posing in a manner that deforms the shirt asillustrated) or may be artist modeled mesh. The transfer mesh 1160 mayrepresent the base mesh conformed to the target mesh, with regions ofdifferent error identified. As an example, regions 1162, 1164, and 1166may respectively represent regions of high, medium, and low positionalerror between the base and target meshes.

Example Rigid Transformations

Avatar processing and rendering system 690 of FIG. 6B, or anothersuitable system, may match a base mesh to a target mesh by transformingor deforming the base mesh using a variety of techniques. As oneexample, matching the base mesh to the target mesh may involve rigidtransformations. Rigid transformations may be applied in regions withhigh degrees of error, high degrees of curvature, or areas with higherror and high curvature. Rigid transformations do not change the sizeor shape of the region of the base mesh being transformed, e.g., thedistances between the vertices in the region are not changed by thetransformation. Examples of rigid transformations that may be applied tothe base mesh to match the target mesh to the target mesh include, butare not limited to, a rigid nearest-neighbor transformation, a rigidnearest-neighbor (NN) transformation with falloff, lineartransformations such as rotation and translation, transformations withor without falloff, and combinations of these and other transformations.Another example of a rigid transformation is an iterative closest point(ICP) algorithm. In other embodiments, the system 690 may utilize anaffine transformation to transform or deform the base mesh.

An example application of a rigid nearest-neighbor transformationprocess 1200 is shown in FIG. 12A. As shown in FIG. 12A, a base mesh1202 may be transformed such that the vertices of the base mesh 1202 arecloser to the vertices of a target mesh 1204. In 1200 a, high errorregions of base mesh 1202 and target mesh 1204 may be determined. In1200 b, avatar processing and rendering system 690 or another suitablesystem may identify the nearest neighbors for some or all of the vertexpoints of the base mesh 1202. As an example, FIG. 12A illustrates thenearest neighbors 1206 on the target mesh 1204 for three differencevertex points on the base mesh 1202. The rigid NN process may utilizethe n nearest neighbors where n is 1, 2, 3, 4, 5, 6, or more. As shownin 1200 c, avatar processing and rendering system 690 or anothersuitable system may then rigidly transform the base mesh 1202 towardsthe target mesh 1204. In other words, the system may attempt to minimizeor optimize the mean of the distances between vertex points on the basemesh 1202 and their corresponding nearest neighbor on the target mesh1204, without altering the shape of the base mesh 1202, but insteadtranslating, rotating, or applying other rigid transformations to thebase mesh 1202.

In at least some embodiments, the rigid transformation of the base mesh1202 towards the target mesh 1204 may be subject to a falloff, whichfeathers the rigid deformations into nearby areas with lower error.Individual regions of the base mesh with high errors may be rigidlytransformed towards the target mesh, while nearby regions with lowererrors are rigidly or non-rigidly transformed with a weighted falloff inthe falloff region. In at least some embodiments, individual regionsthat are rigidly transformed may include a region that is rigidlytransformed (such as the area inside region 1208), perimeter regionsthat are not transformed (such as the area outside region 1210), andintermediate regions (such as the area between regions 1208 and 1210)that are smoothly transitioned between the rigidly transformed region1208 to the area outside region 1210 that may not be transformed (orthat may be transformed using other techniques, including non-rigiddeformation techniques).

A falloff in the rigid transformation may permit regions of high errorto be corrected without the resulting transformations creating new errorin adjacent regions. An example of falloff is illustrated in 1200 b and1200 c of FIG. 12A. In particular, 1200 b illustrates a falloff startregion 1208 and a falloff end region 1210. Within the falloff startregion 1208, the rigid transformation of the base mesh 1202 towards thetarget mesh 1204 may be performed (e.g., the system may attempt tominimize the mean of the distances between nearest neighbors withoutaltering the shape of the transformed mesh). In contrast, the system mayleave the base mesh 1202 relatively unchanged outside region 1210. Inthe intermediate region 1210, the system may transform the base mesh1202 using a weighted falloff of the rigid transformation techniqueapplied inside region 1208 to provide a smooth transition betweenrigidly transformed region 1208 and the portions of base mesh 1202outside region 1210. The weighted falloff of the feathering transitionapplied from the falloff boundary 1208 to the falloff boundary 1210 maybe linear, quadratic, exponential, or any other suitable falloff, invarious embodiments.

The size of region rigidly transformed by the system may, in someembodiments, vary as a function of the initial error between the baseand target meshes 1202 and 1204. As an example, the system may identifythe number n of vertices in the base mesh 1202 that have an error levelabove some threshold. The system may then identify, from the set ofvertices having error levels above the threshold, a subset of verticeshaving the largest errors (e.g., the system may identify n/2 of thevertices as the vertices with the largest errors). The system may thenrigidly transform the subset of vertices with the largest errors (e.g.,the n/2 of the vertices with the largest errors) to match the targetmesh 1204, based on a nearest neighbors approach. In some embodiments,the system may utilize an iterative closest point (ICP) transformationto rigidly transform the subset of vertices with the largest errors tomatch the target mesh 1204.

The size of the fall-off region (e.g., the radii of regions 1208 and1210) may be set based at least partly on the size of the object beingtransformed (e.g., a size of the high error region) or may vary as afunction of the error between the base and target meshes 1202 and 1204.As an example, the falloff radius may be set to π times the square rootof x, with x being equal to the largest error value (e.g., the largestsingle distance between any given vertex in the base mesh 1202 and itscorresponding neighbor in the target mesh 1204) within the region beingrigidly transformed. The fall-off region may serve to feather the rigidnearest neighbor transformation into adjacent vertices having lowererror values (and for which no transformation or a non-rigidtransformation may be applied). In some embodiments, the center of thefall-off sphere may be set to the center of the region being rigidlytransformed. In other embodiments, the center of the fall-off sphere maybe set to the vertex on the target mesh 1204 associated with the largesterror value.

In 1200 c of FIG. 12A, an example output is illustrated, where the inputbase mesh 1202 has been rigidly transformed into shape 1212. Consistentwith the discussion above, the transformation of the input base mesh1202 may be generally rigid inside of the falloff region 1208, notransformation or a non-rigid transformations may be applied outsideregion 1210, and the system may smoothly feather (e.g., in region 1210,but outside 1208) the rigid transformation within region 1208 to the noor non-rigid transformation outside region 1210. Feathering with afalloff region is optional but may provide advantages as will bedescribed with reference to FIG. 13 (e.g., reduction in sharpness orstaircasing of the mesh).

Example Non-Rigid Deformations

Other techniques that may be used, by avatar processing and renderingsystem 690 of FIG. 6B or another suitable system, for matching a basemesh to a target mesh include non-rigid deformations. Non-rigiddeformations may be applied in regions with moderate to low degrees oferror (e.g., distances between vertices on the base mesh andcorresponding vertices on the target mesh) or regions with moderate tolow curvature errors. In non-rigid transformations, distances betweenvertices of the mesh are permitted to change under the transformation,and the size (or shape) of the region may change. Examples of non-rigidtransformations that may be applied to the base mesh to match the targetmesh include, but are not limited to, a closest point on surfacedeformation (CPOS), an iterative closest point on surface deformation,an elastic registration, a physical deformation model (e.g., a modelthat simulates skin), etc. These are merely illustrative examples and,in general, any desired non-rigid (or rigid) deformation may be appliedin areas of low to moderate error.

An example application of a non-rigid closest point on surface (CPOS)process 1250 is shown in FIG. 12B. As shown in FIG. 12B, a base mesh1252 may be deformed (e.g., transformed non-rigidly) such that thevertices of the base mesh 1252 are closer to the vertices of the targetmesh 1254. In at least some embodiments, non-rigid deformations mayconform the base mesh to the target mesh as if the base mesh werevacuum-formed to the target mesh. In 1250 a, medium error regions ofbase mesh 1252 and corresponding regions of target mesh 1254 may beidentified or obtained. In 1250 b and for each vertex in the identifiedregions of the base mesh 1252, the closest point on the surface of thetarget mesh 1254 (e.g., the closest vertex of the target mesh 1254) maybe identified. FIG. 12B illustrates, as examples, the closest points1256 for five different vertices of the base mesh 1252 and the fivecorresponding closest points on the surface of the target mesh 1254.

As shown in 1250 c, avatar processing and rendering system 690 oranother suitable system may then non-rigidly deform the base mesh 1252(e.g., base mesh regions with medium levels of error) towards the targetmesh 1254 (e.g., target mesh regions corresponding to the base meshregions with medium levels of error). In other words, the system mayattempt to reduce, potentially all the way to zero, the distance betweeneach vertex in the base mesh 1252 and the closest point on the targetmesh 1254. If desired, the system may employ an iterative CPOS processin which initial closest points on the surface are calculated, the basemesh vertices are shifted some fraction of the way towards theirrespective closest points on the surface of the target mesh, thenclosest points are recalculated and the base mesh vertices shifted someadditional fraction (or remainder) of the way towards their respectiveclosest points on the surface of the target mesh. As shown in step 1250c, the system may deform the base mesh 1252 until the base mesh 1252 issubstantially conformed to the target mesh 1254 and, as such, theinitial shape of the base mesh 1252 may be transformed into the shape ofthe target mesh 1254.

As with the rigid transformations described in connection with FIG. 12A,the system may (optionally) utilize a falloff in the non-rigiddeformation of FIG. 12B. A falloff in the non-rigid deformation maypermit regions of moderate or medium error to be corrected without theresulting transformations creating new error in adjacent regions oflower error. An example of falloff for the non-rigid transformation ofFIG. 12B is illustrated in 1250 b, which includes a falloff start region1258 and a falloff end region 1260. Within the falloff start region1258, the CPOS deformation process may proceed as described here witheach vertex of the base mesh moving towards the vertex on the targetmesh closest to that base mesh vertex. Outside the falloff end region1260, the system may leave the base mesh 1252 relatively unchanged. Inthe intermediate region, the system may feather the CPOS deformation(e.g., the system may provide more CPOS deformation for vertices closerto falloff start region 1258 and less for vertices closer to the outeredges of the falloff region). The falloff in the CPOS deformation mayhelp to average out the effect of any one vertex, thereby improving theapparent connectivity of vertices in the deformed base mesh 1252,reducing artifacts such as sharpness or staircasing, and reducing theamount of potential error in the matching process. If desired, however,the system may apply a non-rigid deformation without falloff.

Falloff Radius in Non-Rigid Deformations

FIG. 13 illustrates examples of the impact of altering falloff inclosest point on surface (CPOS) deformations, or other non-rigiddeformations.

Image 1300 shows a base mesh. Image 1302 shows the base mesh after aCPOS deformation with no fall off.

Images 1304 and 1306 show the base mesh after CPOS deformations withfall off. As can be seen in images 1304 and 1306, the CPOS deformationswith fall off result in a smoother output (e.g., a smoother matchedmesh).

Image 1308 shows the result of a CPOS deformation without falloff of thebase mesh from image 1300. As shown in image 1308, a single vertex ispulled away from the sphere, which caused a spike to extend from thesphere. If falloff had been applied, the single vertex would appear asimage 1310 instead, which appears more as a rounded bulge.

Example Processes for Matching Target Meshes to Base Meshes usingIterations of Rigid

FIGS. 14A-14C illustrate example processes for matching base meshes totarget meshes. The example process 1400 may be performed by one or morecomputing systems including the remote processing module 270, the localprocessing and data module 260, the avatar processing and renderingsystem 690 of FIG. 6B, the remote computing system 920 of FIG. 9A, othersuitable computing device(s), and combinations of these and othercomputing devices.

In FIG. 14A, at blocks 1401 and 1402, the computing system can obtain abase mesh and a target mesh. As discussed herein, the computing systemcan obtain the base mesh in block 1401 by manipulating a rig of anavatar or object into a desired pose and can obtain the target mesh inblock 1402 by scanning a person (or other target such as an object) inthe desired pose, such as by obtaining photographs, 3D imagery, positioninformation of multiple points on the person's skin and/or clothing (orthe object's surface), etc., or by having an artist sculpt the targetpose.

At blocks 1404 and 1406, the computing system may obtain a set ofdistance errors between the vertices of the base mesh and thecorresponding vertices of the target mesh and may initialize a heatmap.With reference to the example of FIGS. 11A and 11B, a heatmap may takethe form of a graphical representation of the distance errors such asthe heatmaps 1130 and 1160. In other words, the heatmap may visuallyillustrate regions of different levels of error, such as by highlightingregions of high error in red (or single-line hatching), highlightingregions of medium error in green (or double-line hatching), andhighlighting regions of low to no error in blue (or cross-hatching). Ingeneral, other means of distinguishing regions of varying error fromeach other may be used, such as different colors, different intensities,shading, stippling, hatching and cross-hatching, etc. Initializing theheatmap in block 1406 may, if desired, also include segmenting thevertices of the base and target meshes into multiple regions. Inparticular, the regions identified as having relatively high error maybe segmented from other regions and subject to different processingsteps than those other regions. The regions having medium and low errormay similarly be segmented from other regions and subject to differentprocessing steps.

At block 1408, the computing system can check to see how many iterationsof the matching process have been performed. In some embodiments, thecomputing system may terminate the matching process (e.g., as part ofblock 1442) after a predetermined number of iterations of the matchingprocess have been completed. In other embodiments, the computing systemmay terminate the matching process after some other criteria such as ifthe matching process has exceeded some predetermined amount of time.

At block 1410, the computing system may divide the heatmap into groupsbased on error levels (e.g., based on the spectrum of the heatmap or thespectrum of the error levels across the base mesh). Divided regions ofthe heatmap may then be processed differently, accordingly to theirerror level. As examples, block 1410 may include identifying whichregions of the base mesh are low error (e.g., cross-hatched as shown inexample FIGS. 11A and 11B) and passing those regions to block 1412,which regions of the base mesh are medium error or green (or double-linehatched) and passing those regions to block 1416, and which regions ofthe base mesh are high error or red (or single-line hatched) and passingthose regions to block 1424.

At block 1414, the computing system may pass low to no error regions,such as the blue regions of the heatmap, without deformation ortransformation. In other words, the computing system may determine thatthese regions of the base mesh are already well matched to the targetmesh and no further adjustment is needed.

At blocks 1418, 1420, and 1422, the computing system may select elements(e.g., vertices) of the base mesh 1401 that have medium levels of errorrelative to the target mesh 1402 and may apply closest point on thesurface (CPOS) deformation to the selected vertices of the base mesh1401. The CPOS deformation applied by the computing system in block 1422may non-rigidly conform the base mesh 1402 to the target mesh 1402. TheCPOS deformation of block 1422 may be performed as described inconnection with FIG. 12B. The fall off optionally applied in block 1420may also be performed as described in connection with FIG. 12B and FIG.13.

At blocks 1426, 1428, and 1430, the computing system may select elements(e.g., vertices) of the base mesh 1401 that have high levels of errorrelative to the target mesh 1402 and may apply a rigid nearest-neighbortransformation to the selected vertices of the base mesh 1401. The rigidnearest-neighbor transformation applied by the computing system in block1430 may rigidly transform the base mesh 1401 towards the target mesh1402, without deforming the shape of the base mesh 1401. The rigidnearest-neighbor transformation of block 1430 may be performed asdescribed in connection with FIG. 12A and the fall off optionallyapplied in block 1428 may also be performed as described in connectionwith FIG. 12A and FIG. 13.

At block 1432, the computing system may perform a mesh relaxationprocess to address any artifacts introduced by the CPOS deformations andthe rigid nearest-neighbor transformations just performed. As anexample, the computing system may smooth out any spikes in thetransformed base mesh. The relaxation process can comprise a smoothingor filtering process such as a linear or polynomial smoothing process.

At block 1434, the computing system may recalculate error distancesbetween the now-transformed base mesh and the target mesh 1402.

At block 1436, the computing system may optionally update the heatmap bydetermining which regions of the now-transformed base mesh have lowerror levels, medium error levels, and if any remaining areas (e.g.,vertices or groups of vertices) have high error levels.

At block 1438, the computing system may perform a convergence test. Theconvergence test may check to see if the now-transformed base mesh hasbeen sufficiently matched to the target mesh 1202, using any desiredmetrics. As one example, the convergence test of block 1438 may involveidentifying whether or not any vertices, individually or as groups, haveerror values that fall within the medium or high levels. If thecomputing system determines that the now-transformed base meshadequately matched the target mesh (e.g., that all of the vertices haveerror levels that fall within the low to no error range, such as theblue range), the computing system may proceed to block 1442. Otherwise,the computing system may increment an optional counter of the number oftransfer iterations that have been completed as part of block 1440 andreturn to block 1408. As discussed above, once the number of completedtransfer iterations matches (or exceeds) some limit, the computingsystem may proceed to block 1442. If desired, the computing system mayuse a combination of a convergence test and an iteration count todetermine when to proceed to block 1442. As an example, the computingsystem may relax the convergence test, over time or after some number ofiterations, to enable lower-quality matches to pass the convergencetest.

At blocks 1442, 1444, and 1446, the computing system may terminate theprocess of matching base mesh 1401 to target mesh 1402 and may providedesired outputs such as a transfer mesh, blendshape, or other output. Asone example, the computing system may output data indicating howvertices in the base mesh 1401 should be moved relative from theirinitial positions, such that the mesh matches the target mesh 1402.

Examples of Alternative Mesh Transfer Techniques

As an alternative to the above-described arrangement of FIG. 14A, thecomputing system may only transform high error regions in an initial setof iterations, while leaving medium and low error regions uncorrected,and may correct the medium and low error regions in a later set ofiterations. In other words, in FIG. 14B, blocks 1454, 1456, and 1460 maybe omitted for an initial set of iterations, such that only the higherror regions are roughly matched in the initial set of iterations. Theinitial set of iterations may, if desired, continue until all orsubstantially all of the higher error regions have been transformed in amanner that reduces their errors below the high error threshold level(and thus the regions are now classified as low or medium errorregions). Then, after the initial set of iterations, blocks 1454, 1456,and 1460 may be performed and blocks 1458, 1462 may be omitted (asunnecessary, or alternatively included but with an empty group since nohigh error regions exist) in order to finalize the matching of the basemesh to the target mesh.

As yet another alternative, the computing system may divide the inputmeshes into sub-regions based on something other than error. Asexamples, the computing system may divide the input meshes intosub-regions arbitrarily using a geometric pattern, such as checkerboard.Then, the computing system may transform just the high error sub-regions(e.g., the “red” sub-regions in the heatmap) as discussed above or maytransform high and medium error sub-regions simultaneously, as discussedin connection with the example of FIG. 14A. If desired, the computingsystem may divide the input meshes into a checkerboard whereby only afraction (e.g., one-half such as the red spaces of the checkerboard) ofthe regions are directly transformed while the other one-half (e.g., thewhite spaces of the checkerboard) of the regions are adjustedindirectly, such as by linear interpolation (e.g., adjustedautomatically in response to their connection with vertices that weredirectly transformed).

FIG. 14B shows another example process 1450 for matching base meshes totarget meshes. The example process 1450 may be performed by one or morecomputing systems including the remote processing module 270, the localprocessing and data module 260, the avatar processing and renderingsystem 690 of FIG. 6B, the remote computing system 920 of FIG. 9A, othersuitable computing device(s), and combinations of these and othercomputing devices.

At block 1451, the computing system may obtain meshes to match,including at least one base mesh and at least one target mesh.

At block 1452, the computing system may identify regions of errorbetween the meshes and may segregate regions of the mesh based on errorlevels. As an example, regions of high error level may be forwarded toblock 1458, regions of medium error level may be forwarded to block1456, and regions of low or no error level may be forwarded to block1454. Block 1454 may pass regions of low or no error level on to block1464 without transformation.

At blocks 1460 and 1462, the computing system may apply a desiredtransformation or deformation to the base mesh, as part of matching thebase mesh to the target mesh. As an example, the computing system mayapply a rigid transformation in block 1462 to regions of the base meshthat have high error (e.g., are poorly matched with the target mesh) andmay apply a non-rigid deformation in block 1460 to the regions of thebase mesh that have medium error (e.g., are nearly matched with thetarget mesh).

At block 1464, the computing system may test the adjusted base mesh tosee if it is sufficiently matched to the target mesh. Additionally, thecomputing system may determine the iterative matching process shouldcease because a condition has been met, for example, the iterativematching process has exceeded some predetermined number of iterations orsome predetermined length of time or a threshold defining a match hasbeen met. If the computing system determines the base mesh is notsufficiently matched (and other conditions for stopping the process arenot met), the computing system may return to block 1452. If thecomputing system determines that the base mesh is sufficiently matched(or other conditions for stopping the process are met), the computingsystem may proceed to block 1466.

At block 1466, the computing system may output the matched base mesh,which has been matched to the target mesh. In some embodiments, theoutput of the computing system at block 1466 may be referred to as ablendshape.

FIG. 14C shows another example process 1480 for matching base meshes totarget meshes. The example process 1480 may be performed by one or morecomputing systems including the remote processing module 270, the localprocessing and data module 260, the avatar processing and renderingsystem 690 of FIG. 6B, the remote computing system 920 of FIG. 9A, othersuitable computing device(s), and combinations of these and othercomputing devices.

At block 1481, the computing system may receive input meshes such as abase mesh and a target mesh.

At block 1482, the computing system may identify first and secondregions within the base mesh. In some embodiments, the first regions mayrepresent regions (e.g., one or more vertices) of the base mesh havingrelatively high error levels relative to the target mesh and the secondregions may represent regions of the base mesh having relatively lowerror levels. In other embodiments, the first and second regions may beidentified based on factors other than error levels. As examples, thefirst and second regions may be identified by random selection, using aparticular pattern (e.g., applying a checkerboard pattern to the basemesh and identifying the white squares in the checkerboard as the firstregions and the black squares in the checkerboard as the secondregions), or by selecting regions of higher detail (e.g., regions havinga greater density of vertices) as the first regions and regions of lowerdetail as the second regions.

At block 1484, the computing system may apply rigid transformations tothe first regions of the base mesh (e.g., the vertices of the base meshcorresponding to the first regions).

At block 1484, the computing system may apply non-rigid deformations tothe second regions of the base mesh (e.g., the vertices of the base meshcorresponding to the second regions).

At block 1488, the computing system may test to see if thenow-transformed base mesh is sufficiently matched to the target mesh. Ifnot sufficiently matched, the computing system may iterate the matchingprocess, such as by returning to block 1482 as illustrated in FIG. 14C.If sufficiently matched, the computing system may proceed to block 1490and provide an output, such as a transformed base mesh that matches thetarget mesh, sometimes referred to as a blendshape. A convergence testmay be applied such as that the number of iterations exceeds a maximumnumber of iterations or a fiducial error between the meshes (e.g., amean error, a maximum error, etc.) is below a threshold. For example,the threshold may indicate a distance below which the meshes aresufficiently matched (e.g., the threshold distance may be 0.5 cm, 0.1cm, 0.05 cm, 0.01 cm, etc.).

Other Considerations

Each of the processes, methods, and algorithms described herein and/ordepicted in the attached figures may be embodied in, and fully orpartially automated by, code modules executed by one or more physicalcomputing systems, hardware computer processors, application-specificcircuitry, and/or electronic hardware configured to execute specific andparticular computer instructions. For example, computing systems caninclude general purpose computers (e.g., servers) programmed withspecific computer instructions or special purpose computers, specialpurpose circuitry, and so forth. A code module may be compiled andlinked into an executable program, installed in a dynamic link library,or may be written in an interpreted programming language. In someimplementations, particular operations and methods may be performed bycircuitry that is specific to a given function.

Further, certain implementations of the functionality of the presentdisclosure are sufficiently mathematically, computationally, ortechnically complex that application-specific hardware or one or morephysical computing devices (utilizing appropriate specialized executableinstructions) may be necessary to perform the functionality, forexample, due to the volume or complexity of the calculations involved orto provide results substantially in real-time. For example, animationsor video may include many frames, with each frame having millions ofpixels, and specifically programmed computer hardware is necessary toprocess the video data to provide a desired image processing task orapplication in a commercially reasonable amount of time.

Code modules or any type of data may be stored on any type ofnon-transitory computer-readable medium, such as physical computerstorage including hard drives, solid state memory, random access memory(RAM), read only memory (ROM), optical disc, volatile or non-volatilestorage, combinations of the same and/or the like. The methods andmodules (or data) may also be transmitted as generated data signals(e.g., as part of a carrier wave or other analog or digital propagatedsignal) on a variety of computer-readable transmission mediums,including wireless-based and wired/cable-based mediums, and may take avariety of forms (e.g., as part of a single or multiplexed analogsignal, or as multiple discrete digital packets or frames). The resultsof the disclosed processes or process steps may be stored, persistentlyor otherwise, in any type of non-transitory, tangible computer storageor may be communicated via a computer-readable transmission medium.

Any processes, blocks, states, steps, or functionalities in flowdiagrams described herein and/or depicted in the attached figures shouldbe understood as potentially representing code modules, segments, orportions of code which include one or more executable instructions forimplementing specific functions (e.g., logical or arithmetical) or stepsin the process. The various processes, blocks, states, steps, orfunctionalities can be combined, rearranged, added to, deleted from,modified, or otherwise changed from the illustrative examples providedherein. In some embodiments, additional or different computing systemsor code modules may perform some or all of the functionalities describedherein. The methods and processes described herein are also not limitedto any particular sequence, and the blocks, steps, or states relatingthereto can be performed in other sequences that are appropriate, forexample, in serial, in parallel, or in some other manner. Tasks orevents may be added to or removed from the disclosed exampleembodiments. Moreover, the separation of various system components inthe implementations described herein is for illustrative purposes andshould not be understood as requiring such separation in allimplementations. It should be understood that the described programcomponents, methods, and systems can generally be integrated together ina single computer product or packaged into multiple computer products.Many implementation variations are possible.

The processes, methods, and systems may be implemented in a network (ordistributed) computing environment. Network environments includeenterprise-wide computer networks, intranets, local area networks (LAN),wide area networks (WAN), personal area networks (PAN), cloud computingnetworks, crowd-sourced computing networks, the Internet, and the WorldWide Web. The network may be a wired or a wireless network or any othertype of communication network.

The systems and methods of the disclosure each have several innovativeaspects, no single one of which is solely responsible or required forthe desirable attributes disclosed herein. The various features andprocesses described above may be used independently of one another, ormay be combined in various ways. All possible combinations andsubcombinations are intended to fall within the scope of thisdisclosure. Various modifications to the implementations described inthis disclosure may be readily apparent to those skilled in the art, andthe generic principles defined herein may be applied to otherimplementations without departing from the spirit or scope of thisdisclosure. Thus, the claims are not intended to be limited to theimplementations shown herein, but are to be accorded the widest scopeconsistent with this disclosure, the principles and the novel featuresdisclosed herein.

Certain features that are described in this specification in the contextof separate implementations also can be implemented in combination in asingle implementation. Conversely, various features that are describedin the context of a single implementation also can be implemented inmultiple implementations separately or in any suitable subcombination.Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination. No single feature orgroup of features is necessary or indispensable to each and everyembodiment.

Conditional language used herein, such as, among others, “can,” “could,”“might,” “may,” “e.g.,” and the like, unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments include, whileother embodiments do not include, certain features, elements and/orsteps. Thus, such conditional language is not generally intended toimply that features, elements and/or steps are in any way required forone or more embodiments or that one or more embodiments necessarilyinclude logic for deciding, with or without author input or prompting,whether these features, elements and/or steps are included or are to beperformed in any particular embodiment. The terms “comprising,”“including,” “having,” and the like are synonymous and are usedinclusively, in an open-ended fashion, and do not exclude additionalelements, features, acts, operations, and so forth. Also, the term “or”is used in its inclusive sense (and not in its exclusive sense) so thatwhen used, for example, to connect a list of elements, the term “or”means one, some, or all of the elements in the list. In addition, thearticles “a,” “an,” and “the” as used in this application and theappended claims are to be construed to mean “one or more” or “at leastone” unless specified otherwise.

As used herein, a phrase referring to “at least one of” a list of itemsrefers to any combination of those items, including single members. Asan example, “at least one of: A, B, or C” is intended to cover: A, B, C,A and B, A and C, B and C, and A, B, and C. Conjunctive language such asthe phrase “at least one of X, Y and Z,” unless specifically statedotherwise, is otherwise understood with the context as used in generalto convey that an item, term, etc. may be at least one of X, Y or Z.Thus, such conjunctive language is not generally intended to imply thatcertain embodiments require at least one of X, at least one of Y and atleast one of Z to each be present.

Similarly, while operations may be depicted in the drawings in aparticular order, it is to be recognized that such operations need notbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. Further, the drawings may schematically depict one more exampleprocesses in the form of a flowchart. However, other operations that arenot depicted can be incorporated in the example methods and processesthat are schematically illustrated. For example, one or more additionaloperations can be performed before, after, simultaneously, or betweenany of the illustrated operations. Additionally, the operations may berearranged or reordered in other implementations. In certaincircumstances, multitasking and parallel processing may be advantageous.Moreover, the separation of various system components in theimplementations described above should not be understood as requiringsuch separation in all implementations, and it should be understood thatthe described program components and systems can generally be integratedtogether in a single software product or packaged into multiple softwareproducts. Additionally, other implementations are within the scope ofthe following claims. In some cases, the actions recited in the claimscan be performed in a different order and still achieve desirableresults.

EXAMPLES

Various examples of systems that match a base mesh for a virtual avataror object to a target mesh of the virtual avatar or object are describedherein such as the examples enumerated below:

Example 1: A system for matching a base mesh for a virtual avatar to atarget mesh of the virtual avatar, the system comprising: non-transitorycomputer storage configured to store the target mesh for the virtualavatar and the base mesh for the virtual avatar, the target meshcomprising a plurality of target mesh vertices and the base meshcomprising a plurality of base mesh vertices; and a hardware processorin communication with the non-transitory computer storage, the hardwareprocessor programmed to: (i) determine a plurality of error regionsbetween the target mesh and the base mesh, each error regionrepresenting a deviation between positions of the base mesh verticesrelative to positions of the target mesh vertices, the plurality oferror regions comprising a first error region having a first error and asecond error region having a second error, the second error less thanthe first error; (ii) apply a rigid transformation to the base meshvertices in the first error region; (iii) apply a non-rigidtransformation to the base mesh vertices in the second error region;iterate operations (i), (ii), and (iii) until a transformed base meshmeets a convergence criterion; and output a blendshape for the virtualavatar based at least in part on the transformed base mesh.

Example 2: The system of any of the Examples above, wherein the targetmesh is determined from photographic scans of a human or animal subjectperforming a pose.

Example 3: The system of any of the Examples above, wherein theplurality of error regions comprises a third error region having a thirderror that is less than the second error.

Example 4: The system of any of the Examples above, wherein the hardwareprocessor is programmed to not apply a transformation to the base meshvertices in the third region.

Example 5: The system of any of the Examples above, wherein the rigidtransformation comprises a rotation and a translation.

Example 6: The system of any of the Examples above, wherein the rigidtransformation comprises a rigid nearest-neighbor transformation.

Example 7: The system of any of the Examples above, wherein thenon-rigid transformation comprises a closest point on surfacetransformation.

Example 8: The system of any of the Examples above, wherein one or bothof the rigid transformation or the non-rigid transformation comprises afalloff between a first set of vertices and a second set of vertices.

Example 9: The system of any of the Examples above, wherein the hardwareprocessor is further programmed to: determine a first falloff region;determine a second falloff region outside of the first falloff region;apply the rigid transformation or the non-rigid transformation for basemesh vertices in the first falloff region; and feather the rigidtransformation or the non-rigid transformation for base mesh vertices inthe second falloff region.

Example 10: The system of any of the Examples above, wherein thehardware processor is further programmed to not apply a transformationto base mesh vertices outside the second falloff region.

Example 11: The system of any of the Examples above, wherein thehardware processor is programmed to determine a size of the firstfalloff region based at least partly on an error value associated withthe first error region or the second error region.

Example 12: The system of any of the Examples above, wherein thehardware processor is further programmed to relax the base mesh verticesafter completion of operations (ii) and (iii).

Example 13: The system of any of the Examples above, wherein theconvergence criterion comprises a maximum number of iterations or anerror between the transformed base mesh and the target mesh passingbelow a threshold error.

Example 14: The system of any of the Examples above, wherein thehardware processor is further programmed to generate a heatmap based atleast partly on the plurality of error regions.

Example 15: A system for matching a base mesh for a virtual avatar to atarget mesh of the virtual avatar, the system comprising: non-transitorycomputer storage configured to store the target mesh for the virtualavatar and the base mesh for the virtual avatar, the target meshcomprising a plurality of target mesh vertices and the base meshcomprising a plurality of base mesh vertices; and a hardware processorin communication with the non-transitory computer storage, the hardwareprocessor programmed to: identify a first set of regions of the basemesh; identify a second set of regions of the base mesh; apply a rigidtransformation to the base mesh vertices in the first set of regions toprovide a transformed base mesh; and output the transformed base mesh.

Example 16: The system of any of the Examples above, wherein the firstset of regions and the second set of regions form a checkerboardpattern.

Example 17: The system of any of the Examples above, wherein thehardware processor is programmed to interpolate base mesh vertices inthe second set of regions based at least in part on the applied rigidtransformation in the first set of regions.

Example 18: The system of any of the Examples above, wherein thehardware processor is programmed to: apply a non-rigid transformation tobase mesh vertices of the transformed base mesh in the second set ofregions to provide a second transformed base mesh.

Example 19: The system of any of the Examples above, wherein to identifythe first or the second set of regions of the base mesh, the hardwareprocessor is programmed to determine errors between positions of thebase mesh vertices relative to positions of the target mesh vertices.

Example 20: The system of any of the Examples above, wherein the firstset of regions are associated with larger errors than the second set ofregions.

Example 21: The system of any of the Examples above, wherein to applythe rigid transformation to the base mesh vertices, the hardwareprocessor is programmed to: feather the rigid transformation for basemesh vertices between the first set of regions and the second set ofregions.

Example 22: A method for generating at least one blendshape for adigital representation of a deformable object, the method comprising:obtaining, using computing equipment, first and second meshes, each ofwhich comprises a plurality of mesh vertices; matching, using thecomputing equipment, the first mesh to the second mesh by: (i)determining distance differences between the mesh vertices of first andsecond regions of the first mesh relative to the mesh vertices of thesecond mesh; (ii) applying a first type of transformation to the meshvertices in the first region of the first mesh to reduce the distancedifferences associated with mesh vertices in the first region; and (iii)applying a second type of transformation to the mesh vertices in thesecond region of the first mesh to reduce the distance differencesassociated with mesh vertices in the second region; and providing, usingthe computing equipment, a blendshape based at least on the first meshwith transformed mesh vertices.

Example 23: The method of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation and wherein thesecond type of transformation comprises a non-rigid deformation.

Example 24: The method of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation in which a shapeof the first region of the first mesh is maintained and wherein thesecond type of transformation comprises a non-rigid deformation in whicha shape of the second region of the first mesh is altered to match ashape of a given region of the second mesh.

Example 25: The method of any of the Examples above, wherein matchingthe first mesh to the second mesh further comprises determining that thedistance errors associated with the first region are larger thandistance errors associated with the second region.

Example 26: The method of any of the Examples above, wherein matchingthe first mesh to the second mesh further comprises determining that aconvergence criterion is met and wherein providing the blendshapecomprises providing the blendshape upon determining that the convergencecriterion is met.

Example 27: The method of any of the Examples above, wherein determiningthat the convergence criterion is met comprises determining that thedistance differences between mesh vertices of the first mesh relative tocorresponding mesh vertices of the second mesh are less than a givenamount.

Example 28: The method of any of the Examples above, wherein matchingthe first mesh to the second mesh further comprises iterating at leastone of operations (ii) and (iii).

Example 29: The method of any of the Examples above, wherein determiningthat the convergence criterion is met comprises determining that the atleast one of operations (ii) and (iii) has been iterated at least agiven number of times.

Example 30: The method of any of the Examples above, wherein obtaining,using the computing equipment, the first mesh comprises moving a jointin a digital representation of the deformable object.

Example 31: The method of any of the Examples above, wherein obtaining,using the computing equipment, the second mesh comprises scanning areal-world example of the deformable object.

Example 32: The method of any of the Examples above, wherein obtaining,using the computing equipment, the first mesh comprises moving a jointin a digital representation of the deformable object such that thedeformable object is in a given pose.

Example 33: The method of any of the Examples above, wherein obtaining,using the computing equipment, the second mesh comprises scanning areal-world example of the deformable object in the given pose.

Example 34: The method of any of the Examples above, wherein thedeformable object comprises a digital avatar of a human and whereinobtaining, using the computing equipment, the first mesh comprisesmoving the digital avatar of the human into a given pose.

Example 35: The method of any of the Examples above, wherein obtaining,using the computing equipment, the second mesh comprises scanning aperson while the person is in the given pose.

Example 36: A system for generating at least one blendshape for adigital representation of a deformable object, the system comprising:non-transitory computer storage configured to store first and secondmeshes, each of which comprises a plurality of mesh vertices; and ahardware processor configured to match the first mesh to the secondmesh, the hardware processor in communication with the non-transitorycomputer storage and the hardware processor programmed to: (i) determinedistance differences between the mesh vertices of first and secondregions of the first mesh relative to the mesh vertices of the secondmesh; (ii) apply a first type of transformation to the mesh vertices inthe first region of the first mesh to reduce the distance differencesassociated with mesh vertices in the first region; (iii) apply a secondtype of transformation to the mesh vertices in the second region of thefirst mesh to reduce the distance differences associated with meshvertices in the second region; and (iv) provide a blendshape based atleast on the first mesh with transformed mesh vertices.

Example 37: The system of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation and wherein thesecond type of transformation comprises a non-rigid deformation.

Example 38: The system of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation in which a shapeof the first region of the first mesh is maintained and wherein thesecond type of transformation comprises a non-rigid deformation in whicha shape of the second region of the first mesh is altered to match ashape of a given region of the second mesh.

Example 39: The system of any of the Examples above, wherein thehardware processor is programmed to determine that the distance errorsassociated with the first region are larger than distance errorsassociated with the second region.

Example 40: The system of any of the Examples above, wherein thehardware processor is programmed to determine that a convergencecriterion is met and provide the blendshape upon a determination thatthe convergence criterion is met.

Example 41: The system of any of the Examples above, wherein thehardware processor is programmed to determine that the convergencecriterion is met based on a determination that the distance differencesbetween mesh vertices of the first mesh relative to corresponding meshvertices of the second mesh are less than a given amount.

Example 42: The system of any of the Examples above, wherein thehardware processor is programmed to iterate at least one of operations(ii) and (iii) to match the first mesh to the second mesh.

Example 43: The system of any of the Examples above, wherein thehardware processor is programmed to determine that the convergencecriterion is met based on a determination that the at least one ofoperations (ii) and (iii) has been iterated at least a given number oftimes.

Example 44: The system of any of the Examples above, wherein thehardware processor is programmed to move a joint in a digitalrepresentation of the deformable object to obtain the first mesh.

Example 45: The system of any of the Examples above, wherein thehardware processor is programmed to scan a real-world example of thedeformable object to obtain the second mesh.

Example 46: The system of any of the Examples above, wherein thehardware processor is programmed to move a joint in a digitalrepresentation of the deformable object such that the deformable objectis in a given pose to obtain the first mesh.

Example 47: The system of any of the Examples above, wherein thehardware processor is programmed to scan a real-world example of thedeformable object in the given pose to obtain the second mesh.

Example 48: The system of any of the Examples above, wherein thedeformable object comprises a digital avatar of a human and wherein thehardware processor is programmed to move the digital avatar of the humaninto a given pose to obtain the first mesh.

Example 49: The system of any of the Examples above, wherein thehardware processor is programmed to scan a person while the person is inthe given pose to obtain the second mesh.

Example 50: A method for matching a base mesh to a target mesh, themethod comprising: obtaining, using computing equipment, the base andtarget meshes, each of which comprises a plurality of vertices;matching, using the computing equipment, the base mesh to the targetmesh by: (i) applying at least one iteration of a first type oftransformation to at least some of the vertices of the base mesh untildistance differences between the base and target meshes are below afirst threshold; and (ii) applying at least one iteration of a secondtype of transformation to at least some of the vertices of the base meshuntil the distance differences between the base and target meshes arebelow a second threshold, wherein the second threshold is smaller thanthe first threshold; and providing, using the computing equipment, anoutput based at least on the base mesh with transformed vertices.

Example 51: The method of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation of at least someof the vertices of the base mesh towards corresponding vertices of thetarget mesh.

Example 52: The method of any of the Examples above, wherein the rigidtransformation comprises a rigid nearest-neighbor transformation.

Example 53: The method of any of the Examples above, wherein the secondtype of transformation comprises a non-rigid deformation of at leastsome of the vertices of the base mesh towards corresponding vertices ofthe target mesh.

Example 54: The method of any of the Examples above, wherein thenon-rigid deformation comprises a closest point on the surface (CPOS)deformation.

Example 55: The method of any of the Examples above, wherein applying atleast one iteration of the second type of transformation until thedistance differences are below the second threshold comprises: (a)applying the second type of transformation to at least some of thevertices of the base mesh; (b) determining distance differences betweenthe vertices of the base mesh relative to corresponding vertices of thetarget mesh; and iterating operations (a) and (b) until the determineddistance differences have a mean error below the second threshold.

Example 56: The method of any of the Examples above, wherein applying atleast one iteration of the second type of transformation until thedistance differences are below the second threshold comprises: (1)applying the second type of transformation to at least some of thevertices of the base mesh; (2) determining distance differences betweenthe vertices of the base mesh relative to corresponding vertices of thetarget mesh; and iterating operations (1) and (2) until a maximum errorin the determined distance differences is below the second threshold.

Example 57: The method of any of the Examples above, wherein applying atleast one iteration of the second type of transformation until thedistance differences are below the second threshold comprises: (A)applying the second type of transformation to at least some of thevertices of the base mesh; (B) determining distance differences betweenthe vertices of the base mesh relative to corresponding vertices of thetarget mesh; and iterating operations (A) and (B) until a maximum errorin the determined distance differences is less than 0.5 cm.

Example 58: The method of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation of at least someof the vertices of the base mesh towards corresponding vertices of thetarget mesh and wherein applying at least one iteration of the firsttype of transformation comprises: determining a falloff region; applyingthe rigid transformation for the vertices of the base mesh in thefalloff region; and feathering the rigid transformation for the verticesof the base mesh outside of the falloff region.

Example 59: The method of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation of at least someof the vertices of the base mesh towards corresponding vertices of thetarget mesh and wherein applying at least one iteration of the firsttype of transformation comprises: identifying a grouping of vertices ofthe base mesh that have distance differences above the first threshold,wherein a given vertex in the grouping of vertices has the largestdistance difference amongst the grouping; determining a size of afalloff region based at least in part on the magnitude of the largestdistance difference; applying the rigid transformation for the verticesof the base mesh in the falloff region; and feathering the rigidtransformation for the vertices of the base mesh outside of the falloffregion.

Example 60: A system for matching a base mesh to a target mesh, thesystem comprising: non-transitory computer storage configured to storethe base and target meshes, each of which comprises a plurality ofvertices; and a hardware processor configured to match the base mesh tothe target mesh, the hardware processor in communication with thenon-transitory computer storage and the hardware processor programmedto: (i) apply at least one iteration of a first type of transformationto at least some of the vertices of the base mesh until distancedifferences between the base and target meshes are below a firstthreshold; (ii) apply at least one iteration of a second type oftransformation to at least some of the vertices of the base mesh untilthe distance differences between the base and target meshes are below asecond threshold, wherein the second threshold is smaller than the firstthreshold; and (iii) provide an output based at least on the base meshwith transformed vertices.

Example 61: The system of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation of at least someof the vertices of the base mesh towards corresponding vertices of thetarget mesh.

Example 62: The system of any of the Examples above, wherein the rigidtransformation comprises a rigid nearest-neighbor transformation.

Example 63: The system of any of the Examples above, wherein the secondtype of transformation comprises a non-rigid deformation of at leastsome of the vertices of the base mesh towards corresponding vertices ofthe target mesh.

Example 64: The system of any of the Examples above, wherein thenon-rigid deformation comprises a closest point on the surface (CPOS)deformation.

Example 65: The system of any of the Examples above, wherein thehardware processor is programmed to: (a) apply the second type oftransformation to at least some of the vertices of the base mesh; (b)determine distance differences between the vertices of the base meshrelative to corresponding vertices of the target mesh; and iterateoperations (a) and (b) until the determined distance differences have amean error below the second threshold.

Example 66: The system of any of the Examples above, wherein thehardware processor is programmed to: (1) apply the second type oftransformation to at least some of the vertices of the base mesh; (2)determine distance differences between the vertices of the base meshrelative to corresponding vertices of the target mesh; and iterateoperations (1) and (2) until a maximum error in the determined distancedifferences is below the second threshold.

Example 67: The system of any of the Examples above, wherein thehardware processor is programmed to: (A) apply the second type oftransformation to at least some of the vertices of the base mesh; (B)determine distance differences between the vertices of the base meshrelative to corresponding vertices of the target mesh; and iterateoperations (A) and (B) until a maximum error in the determined distancedifferences is less than 0.5 cm.

Example 68: The system of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation of at least someof the vertices of the base mesh towards corresponding vertices of thetarget mesh and wherein the hardware processor is programmed to:determine a falloff region; apply the rigid transformation for thevertices of the base mesh in the falloff region; and feather the rigidtransformation for the vertices of the base mesh outside of the falloffregion.

Example 69: The system of any of the Examples above, wherein the firsttype of transformation comprises a rigid transformation of at least someof the vertices of the base mesh towards corresponding vertices of thetarget mesh and wherein the hardware processor is programmed to:identify a grouping of vertices of the base mesh that have distancedifferences above the first threshold, wherein a given vertex in thegrouping of vertices has the largest distance difference amongst thegrouping; determine a size of a falloff region based at least in part onthe magnitude of the largest distance difference; apply the rigidtransformation for the vertices of the base mesh in the falloff region;and feather the rigid transformation for the vertices of the base meshoutside of the falloff region.

Example 70: A method for matching a base mesh to a target mesh, themethod comprising: obtaining, using computing equipment, base and targetmeshes, each of which comprises a plurality of vertices; matching, usingthe computing equipment, the base mesh to the target mesh by: (i)determining distance differences between at least some of the verticesof the base mesh relative to the target mesh; (ii) identifying a set ofvertices in the base mesh that have distance differences above a firstthreshold; (iii) applying a rigid transformation to the set of verticesin the base mesh to reduce the distance differences of the vertices inthe set of vertices and to produce a first transformed base mesh; and(iv) applying a non-rigid deformation to the set of vertices in thefirst transformed base mesh to further reduce the distance differencesof the vertices in the set of vertices and to produce a secondtransformed base mesh; and providing, using the computing equipment, ablendshape based at least on the second transformed base mesh.

Example 71: The method of any of the Examples above, wherein a shape ofthe set of vertices is maintained while applying the rigidtransformation.

Example 72: The method of any of the Examples above, wherein a shape ofthe set of vertices is altered to match a shape of correspondingvertices of the target mesh.

Example 73: The method of any of the Examples above, wherein applyingthe rigid transformation to the set of vertices comprises: (1) applyingthe rigid transformation to at least some of vertices of the set ofvertices; (2) determining distance differences between the vertices ofthe set of vertices relative to corresponding vertices of the targetmesh; and iterating operations (1) and (2) until a first convergencecriterion is satisfied.

Example 74: The method of any of the Examples above, wherein the firstconvergence criterion comprises a criterion that a maximum error in thedetermined distance differences is below the first threshold.

Example 75: The method of any of the Examples above, wherein the firstconvergence criterion comprises a criterion that a maximum number ofiterations of operations (1) and (2) have been completed.

Example 76: The method of any of the Examples above, wherein applyingthe non-rigid transformation to the set of vertices comprises: (a)applying the non-rigid transformation to at least some of vertices ofthe set of vertices; (b) determining distance differences between thevertices of the set of vertices relative to corresponding vertices ofthe target mesh; and iterating operations (a) and (b) until a secondconvergence criterion is satisfied.

Example 77: The method of any of the Examples above, wherein the secondconvergence criterion comprises a criterion that a maximum error in thedetermined distance differences is below a second threshold.

Example 78: The method of any of the Examples above, wherein the secondconvergence criterion comprises a criterion that a maximum number ofiterations of operations (a) and (b) have been completed.

Example 79: A system for matching a base mesh to a target mesh, thesystem comprising: non-transitory computer storage configured to storethe base and target meshes, each of which comprises a plurality ofvertices; and a hardware processor configured to match the base mesh tothe target mesh, the hardware processor in communication with thenon-transitory computer storage and the hardware processor programmedto: (i) determine distance differences between at least some of thevertices of the base mesh relative to the target mesh; (ii) identify aset of vertices in the base mesh that have distance differences above afirst threshold; (iii) apply a rigid transformation to the set ofvertices in the base mesh to reduce the distance differences of thevertices in the set of vertices and to produce a first transformed basemesh; (iv) apply a non-rigid deformation to the set of vertices in thefirst transformed base mesh to further reduce the distance differencesof the vertices in the set of vertices and to produce a secondtransformed base mesh; and (v) provide a blendshape based at least onthe second transformed base mesh.

Example 80: The system of any of the Examples above, wherein thehardware processor is programmed to maintain a shape of the set ofvertices while applying the rigid transformation.

Example 81: The system of any of the Examples above, wherein thehardware processor is programmed to alter a shape of the set of verticesto match a shape of corresponding vertices of the target mesh.

Example 82: The system of any of the Examples above, wherein thehardware processor is programmed to: (1) apply the rigid transformationto at least some of vertices of the set of vertices; (2) determinedistance differences between the vertices of the set of verticesrelative to corresponding vertices of the target mesh; and iterateoperations (1) and (2) until a first convergence criterion is satisfied.

Example 83: The system of any of the Examples above, wherein the firstconvergence criterion comprises a criterion that a maximum error in thedetermined distance differences is below the first threshold.

Example 84: The system of any of the Examples above, wherein the firstconvergence criterion comprises a criterion that a maximum number ofiterations of operations (1) and (2) have been completed.

Example 85: The system of any of the Examples above, wherein thehardware processor is programmed to: (a) apply the non-rigidtransformation to at least some of vertices of the set of vertices; (b)determine distance differences between the vertices of the set ofvertices relative to corresponding vertices of the target mesh; anditerate operations (a) and (b) until a second convergence criterion issatisfied.

Example 86: The system of any of the Examples above, wherein the secondconvergence criterion comprises a criterion that a maximum error in thedetermined distance differences is below a second threshold.

Example 87: The system of any of the Examples above, wherein the secondconvergence criterion comprises a criterion that a maximum number ofiterations of operations (a) and (b) have been completed.

Any of the above Examples can be combined. Additionally, any of theabove Examples can be implemented with a single depth plane and/or withone or more variable depth planes (i.e., one or more elements withvariable focusing power that provide accommodation cues that vary overtime).

What is claimed is:
 1. A system for matching a base mesh for a virtualavatar to a target mesh of the virtual avatar, the system comprising:non-transitory computer storage configured to store the target mesh forthe virtual avatar and the base mesh for the virtual avatar, the targetmesh comprising a plurality of target mesh vertices and the base meshcomprising a plurality of base mesh vertices; and a hardware processorin communication with the non-transitory computer storage, the hardwareprocessor programmed to: (i) (i) determine a plurality of error regionsbetween the target mesh and the base mesh, each error regionrepresenting a deviation between positions of the base mesh verticesrelative to positions of the target mesh vertices, the plurality oferror regions comprising a first error region having a first error and asecond error region having a second error, the second error less thanthe first error; (ii) (ii) apply a rigid transformation to the base meshvertices in the first error region; (iii) (iii) apply a non-rigidtransformation to the base mesh vertices in the second error region;(iv) iterate operations (i), (ii), and (iii) until a transformed basemesh meets a convergence criterion; and (v) output a blendshape for thevirtual avatar based at least in part on the transformed base mesh. 2.The system of claim 1, wherein the target mesh is determined fromphotographic scans of a human or animal subject performing a pose. 3.The system of claim 1, wherein the plurality of error regions comprisesa third error region having a third error that is less than the seconderror.
 4. The system of claim 3, wherein the hardware processor isprogrammed to not apply a transformation to the base mesh vertices inthe third region.
 5. The system of claim 1, wherein the rigidtransformation comprises a rotation and a translation.
 6. The system ofclaim 1, wherein the rigid transformation comprises a rigidnearest-neighbor transformation.
 7. The system of claim 1, wherein thenon-rigid transformation comprises a closest point on surfacetransformation.
 8. The system of claim 1, wherein one or both of therigid transformation or the non-rigid transformation comprises a falloffbetween a first set of vertices and a second set of vertices.
 9. Thesystem of claim 1, wherein the hardware processor is further programmedto: determine a first falloff region; determine a second falloff regionoutside of the first falloff region; apply the rigid transformation orthe non-rigid transformation for base mesh vertices in the first falloffregion; and feather the rigid transformation or the non-rigidtransformation for base mesh vertices in the second falloff region. 10.The system of claim 9, wherein the hardware processor is furtherprogrammed to not apply a transformation to base mesh vertices outsidethe second falloff region.
 11. The system of claim 9, wherein thehardware processor is programmed to determine a size of the firstfalloff region based at least partly on an error value associated withthe first error region or the second error region.
 12. The system claim1, wherein the hardware processor is further programmed to relax thebase mesh vertices after completion of operations (ii) and (iii). 13.The system of claim 1, wherein the convergence criterion comprises amaximum number of iterations or an error between the transformed basemesh and the target mesh passing below a threshold error.
 14. The systemof claim 1, wherein the hardware processor is further programmed togenerate a heatmap based at least partly on the plurality of errorregions.
 15. A system for matching a base mesh for a virtual avatar to atarget mesh of the virtual avatar, the system comprising: non-transitorycomputer storage configured to store the target mesh for the virtualavatar and the base mesh for the virtual avatar, the target meshcomprising a plurality of target mesh vertices and the base meshcomprising a plurality of base mesh vertices; and a hardware processorin communication with the non-transitory computer storage, the hardwareprocessor programmed to: (i) identify a first set of regions of the basemesh; (ii) identify a second set of regions of the base mesh; (iii)apply a rigid transformation to the base mesh vertices in the first setof regions to provide a transformed base mesh; and (iv) output thetransformed base mesh.
 16. A method for generating at least oneblendshape for a digital representation of a deformable object, themethod comprising: obtaining, using computing equipment, first andsecond meshes, each of which comprises a plurality of mesh vertices;matching, using the computing equipment, the first mesh to the secondmesh by: (i) determining distance differences between the mesh verticesof first and second regions of the first mesh relative to the meshvertices of the second mesh; (ii) applying a first type oftransformation to the mesh vertices in the first region of the firstmesh to reduce the distance differences associated with mesh vertices inthe first region; and (iii) applying a second type of transformation tothe mesh vertices in the second region of the first mesh to reduce thedistance differences associated with mesh vertices in the second region;and providing, using the computing equipment, a blendshape based atleast on the first mesh with transformed mesh vertices.
 17. A system forgenerating at least one blendshape for a digital representation of adeformable object, the system comprising: non-transitory computerstorage configured to store first and second meshes, each of whichcomprises a plurality of mesh vertices; and a hardware processorconfigured to match the first mesh to the second mesh, the hardwareprocessor in communication with the non-transitory computer storage andthe hardware processor programmed to: (i) determine distance differencesbetween the mesh vertices of first and second regions of the first meshrelative to the mesh vertices of the second mesh; (ii) apply a firsttype of transformation to the mesh vertices in the first region of thefirst mesh to reduce the distance differences associated with meshvertices in the first region; (iii) apply a second type oftransformation to the mesh vertices in the second region of the firstmesh to reduce the distance differences associated with mesh vertices inthe second region; and (iv) provide a blendshape based at least on thefirst mesh with transformed mesh vertices.
 18. A method for matching abase mesh to a target mesh, the method comprising: obtaining, usingcomputing equipment, the base and target meshes, each of which comprisesa plurality of vertices; matching, using the computing equipment, thebase mesh to the target mesh by: (i) applying at least one iteration ofa first type of transformation to at least some of the vertices of thebase mesh until distance differences between the base and target meshesare below a first threshold; and (ii) applying at least one iteration ofa second type of transformation to at least some of the vertices of thebase mesh until the distance differences between the base and targetmeshes are below a second threshold, wherein the second threshold issmaller than the first threshold; and providing, using the computingequipment, an output based at least on the base mesh with transformedvertices.
 19. A system for matching a base mesh to a target mesh, thesystem comprising: non-transitory computer storage configured to storethe base and target meshes, each of which comprises a plurality ofvertices; and a hardware processor configured to match the base mesh tothe target mesh, the hardware processor in communication with thenon-transitory computer storage and the hardware processor programmedto: (i) apply at least one iteration of a first type of transformationto at least some of the vertices of the base mesh until distancedifferences between the base and target meshes are below a firstthreshold; (ii) apply at least one iteration of a second type oftransformation to at least some of the vertices of the base mesh untilthe distance differences between the base and target meshes are below asecond threshold, wherein the second threshold is smaller than the firstthreshold; and (iii) provide an output based at least on the base meshwith transformed vertices.
 20. A method for matching a base mesh to atarget mesh, the method comprising: obtaining, using computingequipment, base and target meshes, each of which comprises a pluralityof vertices; matching, using the computing equipment, the base mesh tothe target mesh by: (i) determining distance differences between atleast some of the vertices of the base mesh relative to the target mesh;(ii) identifying a set of vertices in the base mesh that have distancedifferences above a first threshold; (iii) applying a rigidtransformation to the set of vertices in the base mesh to reduce thedistance differences of the vertices in the set of vertices and toproduce a first transformed base mesh; and (iv) applying a non-rigiddeformation to the set of vertices in the first transformed base mesh tofurther reduce the distance differences of the vertices in the set ofvertices and to produce a second transformed base mesh; and providing,using the computing equipment, a blendshape based at least on the secondtransformed base mesh.
 21. A system for matching a base mesh to a targetmesh, the system comprising: non-transitory computer storage configuredto store the base and target meshes, each of which comprises a pluralityof vertices; and a hardware processor configured to match the base meshto the target mesh, the hardware processor in communication with thenon-transitory computer storage and the hardware processor programmedto: (i) determine distance differences between at least some of thevertices of the base mesh relative to the target mesh; (ii) identify aset of vertices in the base mesh that have distance differences above afirst threshold; (iii) apply a rigid transformation to the set ofvertices in the base mesh to reduce the distance differences of thevertices in the set of vertices and to produce a first transformed basemesh; (iv) apply a non-rigid deformation to the set of vertices in thefirst transformed base mesh to further reduce the distance differencesof the vertices in the set of vertices and to produce a secondtransformed base mesh; and (v) provide a blendshape based at least onthe second transformed base mesh.